diff --git a/.gitignore b/.gitignore index 956def6..674de73 100644 --- a/.gitignore +++ b/.gitignore @@ -26,3 +26,7 @@ wheels/ bandit-report.json pip-audit-report.json sbom.json + +# Example data and results (download from releases) +examples/anthropic_comparison/*.json +examples/anthropic_comparison/*.png diff --git a/Taskfile.yml b/Taskfile.yml index 9519fab..7081d3a 100644 --- a/Taskfile.yml +++ b/Taskfile.yml @@ -4,7 +4,7 @@ tasks: install: desc: Install dependencies cmds: - - uv sync --dev --all-packages --group security + - uv sync --dev --all-packages --group security --group examples lint: desc: Run linting with ruff diff --git a/examples/anthropic_comparison/README.md b/examples/anthropic_comparison/README.md new file mode 100644 index 0000000..6381c1a --- /dev/null +++ b/examples/anthropic_comparison/README.md @@ -0,0 +1,69 @@ +# Anthropic Comparison Example + +This example compares MCP Optimizer's semantic tool search against Anthropic's native tool search approaches (BM25, regex, and hybrid). + +## Setup + +### Download Required Data Files + +The example requires test data files that are excluded from version control. Download them from the GitHub release: + +```bash +./download_data.sh +``` + +This downloads: +- `mcp_tools_cleaned.json` (786K) - Tool definitions +- `mcp_tools_cleaned_tests_claude-sonnet-4.json` (1.2M) - Test cases +- `results.json` (13M) - Pre-generated comparison results +- `results_accuracy_comparison.png` (154K) - Results visualization + +**Note:** If you want to regenerate results yourself, you only need the first two files. The script will skip files that already exist locally. + +## Prerequisites + +- `ANTHROPIC_API_KEY` environment variable set +- `OPENROUTER_API_KEY` environment variable set + +## Running the Comparison + +### 1. Ingest Test Data + +First, load the test tools into the MCP Optimizer database: + +```bash +uv run python ingest_test_data.py +``` + +This creates `mcp_optimizer_test.db` with all test tools and their embeddings. + +### 2. Run Comparison + +Execute the comparison across all test cases: + +```bash +uv run python tool_search_comparison.py +``` + +Results are saved to `./results.json`. + +## Options + +The comparison script supports several options: + +```bash +uv run python tool_search_comparison.py --help +``` + +Key options: +- `--limit N` - Limit to N test cases (useful for testing) +- `--max-concurrency N` - Set concurrent test executions (default: 10) +- `--resume` - Resume from existing results, retrying only failed tests +- `--output-file PATH` - Custom output file path + +## Output + +The comparison generates: +- JSON report with detailed metrics +- Console summary with aggregate statistics +- Per-test-case accuracy, token usage, and retrieval metrics diff --git a/examples/anthropic_comparison/comparison_orchestrator.py b/examples/anthropic_comparison/comparison_orchestrator.py new file mode 100644 index 0000000..02803ab --- /dev/null +++ b/examples/anthropic_comparison/comparison_orchestrator.py @@ -0,0 +1,574 @@ +"""Orchestrator for coordinating comparison evaluation.""" + +import asyncio +import json +from datetime import datetime +from pathlib import Path + +import structlog +from mcp_optimizer_agent import McpOptimizerAgentRunner +from metrics import MetricsComputer +from models import ComparisonReport, ComparisonResult, TestCase, TestDataset +from native_approach import NativeApproachRunner +from results_exporter import ResultsExporter +from tool_converter import ToolConverter + +logger = structlog.get_logger(__name__) + + +class ComparisonOrchestrator: + """Orchestrates comparison between Native and MCP Optimizer approaches.""" + + def __init__( + self, + dataset_path: Path, + anthropic_api_key: str, + openrouter_api_key: str, + limit: int | None = None, + max_concurrency: int = 10, + llm_model: str = "anthropic/claude-sonnet-4", + resume_from: Path | None = None, + output_file: Path | None = None, + ): + """Initialize orchestrator. + + Args: + dataset_path: Path to test dataset JSON + anthropic_api_key: Anthropic API key + openrouter_api_key: OpenRouter API key + limit: Optional limit on test cases + max_concurrency: Max concurrent test executions + llm_model: LLM model for MCP Optimizer agent + resume_from: Optional path to existing results JSON to resume from + output_file: Optional path to output file for saving partial results + """ + self.dataset_path = dataset_path + self.anthropic_api_key = anthropic_api_key + self.openrouter_api_key = openrouter_api_key + self.limit = limit + self.max_concurrency = max_concurrency + self.llm_model = llm_model + self.resume_from = resume_from + self.output_file = output_file + self.completed_count = 0 + self.total_count = 0 + self.baseline_all_tools_tokens: int | None = None + + def dry_run(self) -> bool: + """Validate setup without running comparison. + + Returns: + True if all validations pass + + Raises: + ValueError: If any validation fails + """ + logger.info("Running dry-run validation") + + # 1. Check dataset exists and can be loaded + try: + logger.info("Validating dataset", path=str(self.dataset_path)) + if not self.dataset_path.exists(): + raise ValueError(f"Dataset not found: {self.dataset_path}") + + dataset = TestDataset.load_from_json(self.dataset_path, self.limit) + logger.info( + "Dataset valid", + total_cases=len(dataset.cases), + limited_to=self.limit or "all", + ) + except Exception as e: + raise ValueError(f"Failed to load dataset: {e}") from e + + # 2. Check MCP tools file exists + mcp_tools_path = self.dataset_path.parent / "mcp_tools_cleaned.json" + logger.info("Validating MCP tools file", path=str(mcp_tools_path)) + if not mcp_tools_path.exists(): + raise ValueError(f"MCP tools file not found: {mcp_tools_path}") + + # 3. Try to load and convert tools + try: + all_tools = self._load_and_convert_tools() + logger.info("MCP tools loaded and converted", count=len(all_tools)) + except Exception as e: + raise ValueError(f"Failed to load/convert MCP tools: {e}") from e + + # 4. Check database path is accessible + test_db_path = Path(__file__).parent / "mcp_optimizer_test.db" + logger.info("Validating database path", path=str(test_db_path)) + if test_db_path.exists(): + logger.info("Database file exists") + else: + logger.info("Database will be created at", path=str(test_db_path)) + + # 5. Check output file path is writable + if self.output_file: + logger.info("Validating output path", path=str(self.output_file)) + self.output_file.parent.mkdir(parents=True, exist_ok=True) + logger.info("Output directory is writable") + + # 6. Check resume file if specified + if self.resume_from: + logger.info("Validating resume file", path=str(self.resume_from)) + if not self.resume_from.exists(): + raise ValueError(f"Resume file not found: {self.resume_from}") + try: + existing_results = self._load_existing_results() + logger.info("Resume file valid", count=len(existing_results)) + except Exception as e: + raise ValueError(f"Failed to load resume file: {e}") from e + + logger.info("Dry-run validation passed") + return True + + async def run(self) -> ComparisonReport: + """Execute full comparison workflow. + + Returns: + ComparisonReport with results + """ + logger.info("Starting comparison", dataset=str(self.dataset_path)) + + # 1. Load test dataset + dataset = TestDataset.load_from_json(self.dataset_path, self.limit) + self.total_count = len(dataset.cases) + logger.info( + "Loaded test dataset", + total_cases=self.total_count, + ) + + # 2. Load existing results if resuming + existing_results = {} + if self.resume_from: + existing_results = self._load_existing_results() + logger.info( + "Loaded existing results", + count=len(existing_results), + successful=sum( + 1 for r in existing_results.values() if self._result_fully_succeeded(r) + ), + errored=sum(1 for r in existing_results.values() if self._result_has_errors(r)), + ) + # Check if baseline was already measured + try: + with open(self.resume_from) as f: + data = json.load(f) + self.baseline_all_tools_tokens = data.get("baseline_all_tools_tokens") + if self.baseline_all_tools_tokens: + logger.info( + "Using existing baseline measurement", + tokens=self.baseline_all_tools_tokens, + ) + except Exception: + pass + + # 3. Load and convert MCP tools for native approach + logger.info("Loading and converting MCP tools") + all_tools = self._load_and_convert_tools() + logger.info("Converted tools", count=len(all_tools)) + + # 4. Initialize runners + logger.info("Initializing runners") + native_runner = NativeApproachRunner(api_key=self.anthropic_api_key, all_tools=all_tools) + + # 5. Measure baseline token usage (only if not already measured) + if self.baseline_all_tools_tokens is None: + logger.info("Measuring baseline token usage with all tools loaded") + self.baseline_all_tools_tokens = await native_runner.measure_baseline_all_tools_tokens() + + # Use test database in examples/anthropic_comparison directory + test_db_path = Path(__file__).parent / "mcp_optimizer_test.db" + mcp_agent_runner = McpOptimizerAgentRunner( + llm_model=self.llm_model, test_db_path=test_db_path + ) + + # 6. Run comparison on all test cases + logger.info("Running comparison on test cases") + results = await self._run_comparison( + dataset.cases, native_runner, mcp_agent_runner, existing_results + ) + + # 7. Compute aggregate metrics + logger.info("Computing aggregate metrics") + aggregate_metrics = MetricsComputer.compute_aggregate_metrics(results) + + # 8. Return report + report = ComparisonReport( + aggregate_metrics=aggregate_metrics, + individual_results=results, + dataset_path=str(self.dataset_path), + num_test_cases=len(dataset.cases), + timestamp=datetime.now().isoformat(), + mcp_optimizer_model=self.llm_model, + baseline_all_tools_tokens=self.baseline_all_tools_tokens, + ) + + logger.info("Comparison complete") + return report + + def _load_existing_results(self) -> dict[str, "ComparisonResult"]: + """Load existing results from JSON file. + + Returns: + Dictionary mapping query to ComparisonResult + """ + if not self.resume_from or not self.resume_from.exists(): + return {} + + try: + with open(self.resume_from) as f: + data = json.load(f) + + from models import ComparisonReport + + report = ComparisonReport.model_validate(data) + + # Create query -> result mapping + return {result.test_case.query: result for result in report.individual_results} + + except Exception as e: + logger.error( + "Failed to load existing results", error=str(e), path=str(self.resume_from) + ) + return {} + + def _save_partial_results(self, results: list[ComparisonResult]) -> None: + """Save partial results to output file. + + Args: + results: List of comparison results to save + """ + if not self.output_file or not results: + return + + try: + # Compute aggregate metrics for current results + aggregate_metrics = MetricsComputer.compute_aggregate_metrics(results) + + # Create partial report + report = ComparisonReport( + aggregate_metrics=aggregate_metrics, + individual_results=results, + dataset_path=str(self.dataset_path), + num_test_cases=self.total_count, + timestamp=datetime.now().isoformat(), + mcp_optimizer_model=self.llm_model, + baseline_all_tools_tokens=self.baseline_all_tools_tokens, + ) + + # Save to file + ResultsExporter.save_json_report(report, self.output_file) + logger.info( + "Saved partial results", + completed=len(results), + total=self.total_count, + path=str(self.output_file), + ) + + except Exception as e: + logger.error("Failed to save partial results", error=str(e)) + + def _get_progress_string(self) -> str: + """Get formatted progress string. + + Returns: + Progress string in format "completed/total (percentage%)" + """ + progress_pct = (self.completed_count / self.total_count) * 100 + return f"{self.completed_count}/{self.total_count} ({progress_pct:.1f}%)" + + @staticmethod + def _result_fully_succeeded(result: ComparisonResult) -> bool: + """Check if all approaches in a comparison result succeeded. + + Args: + result: Comparison result to check + + Returns: + True if all native and MCP approaches succeeded (no errors) + """ + all_native_succeeded = all(nr.error is None for nr in result.native_results.values()) + mcp_succeeded = result.mcp_optimizer_result.error is None + return all_native_succeeded and mcp_succeeded + + @staticmethod + def _result_has_errors(result: ComparisonResult) -> bool: + """Check if any approach in a comparison result failed. + + Args: + result: Comparison result to check + + Returns: + True if any native or MCP approach has an error + """ + any_native_failed = any(nr.error is not None for nr in result.native_results.values()) + mcp_failed = result.mcp_optimizer_result.error is not None + return any_native_failed or mcp_failed + + def _load_and_convert_tools(self) -> list[dict]: + """Load MCP tools and convert to Anthropic format. + + Returns: + List of tools in Anthropic format + """ + # Load from same directory as dataset + mcp_tools_path = self.dataset_path.parent / "mcp_tools_cleaned.json" + + with open(mcp_tools_path) as f: + mcp_servers = json.load(f) + + converter = ToolConverter() + all_tools = [] + + for server in mcp_servers: + for tool in server["tools"]: + anthropic_tool = converter.convert_tool(server, tool) + all_tools.append(anthropic_tool.model_dump()) + + return all_tools + + def _handle_native_exceptions(self, native_results: dict, test_case: TestCase) -> dict: + """Handle exceptions in native search results. + + Args: + native_results: Dictionary of native approach results + test_case: Test case being run + + Returns: + Dictionary with exceptions converted to error results + """ + from models import NativeSearchResult + + handled_results = {} + for approach, result in native_results.items(): + if isinstance(result, Exception): + logger.error( + "Native search failed", + error=str(result), + query=test_case.query, + approach=approach, + ) + handled_results[approach] = NativeSearchResult( + test_case=test_case, + approach=approach, + selected_tool_name=None, + retrieved_tools=[], + num_tools_returned=0, + search_time_s=0.0, + request_tokens=0, + response_tokens=0, + total_tokens=0, + error=str(result), + ) + else: + handled_results[approach] = result + return handled_results + + async def _retry_failed_approaches( + self, + test_case: TestCase, + existing: ComparisonResult, + native_runner: NativeApproachRunner, + mcp_runner: McpOptimizerAgentRunner, + ) -> tuple[dict, any]: + """Retry only the failed approaches from an existing result. + + Args: + test_case: Test case to run + existing: Existing comparison result with some failures + native_runner: Native approach runner + mcp_runner: MCP Optimizer agent runner + + Returns: + Tuple of (native_results dict, mcp_result) + """ + # Determine which runners to execute + tasks = [] + native_approaches_to_run = [] + + for approach in native_runner.APPROACHES: + if ( + approach not in existing.native_results + or existing.native_results[approach].error is not None + ): + native_approaches_to_run.append(approach) + tasks.append(native_runner.search_tool(test_case, approach)) + + if existing.mcp_optimizer_result.error is not None: + tasks.append(mcp_runner.search_tool(test_case)) + need_mcp = True + else: + need_mcp = False + + results = await asyncio.gather(*tasks, return_exceptions=True) + + # Parse results + native_results = {} + approach_idx = 0 + for approach in native_runner.APPROACHES: + if approach in native_approaches_to_run: + native_results[approach] = results[approach_idx] + approach_idx += 1 + else: + native_results[approach] = existing.native_results[approach] + + if need_mcp: + mcp_result = results[approach_idx] + else: + mcp_result = existing.mcp_optimizer_result + + return native_results, mcp_result + + def _handle_mcp_exception(self, mcp_result, test_case: TestCase): + """Handle exception in MCP search result. + + Args: + mcp_result: MCP search result or exception + test_case: Test case being run + + Returns: + Valid MCP result with error if exception occurred + """ + if isinstance(mcp_result, Exception): + logger.error("MCP search failed", error=str(mcp_result), query=test_case.query) + from models import McpOptimizerSearchResult + + return McpOptimizerSearchResult( + test_case=test_case, + selected_server_name=None, + selected_tool_name=None, + retrieved_tools=[], + search_time_s=0.0, + request_tokens=0, + response_tokens=0, + total_tokens=0, + num_tools_returned=0, + error=str(mcp_result), + ) + return mcp_result + + async def _run_single_test_case( + self, + test_case: TestCase, + native_runner: NativeApproachRunner, + mcp_runner: McpOptimizerAgentRunner, + existing_results: dict[str, ComparisonResult], + semaphore: asyncio.Semaphore, + ) -> ComparisonResult: + """Run all approaches on a single test case. + + Args: + test_case: Test case to run + native_runner: Native approach runner + mcp_runner: MCP Optimizer agent runner + existing_results: Dictionary of existing results to skip/retry + semaphore: Semaphore for concurrency control + + Returns: + Comparison result for the test case + """ + async with semaphore: + # Check if we already have successful results for this test case + if test_case.query in existing_results: + existing = existing_results[test_case.query] + + # Skip if all approaches succeeded + if self._result_fully_succeeded(existing): + self.completed_count += 1 + logger.info( + "Skipping completed test case", + query=test_case.query[:50] + "...", + progress=self._get_progress_string(), + ) + return existing + + # Retry only the approach(es) that failed + self.completed_count += 1 + logger.info( + "Retrying errored test case", + query=test_case.query[:50] + "...", + progress=self._get_progress_string(), + ) + + native_results, mcp_result = await self._retry_failed_approaches( + test_case, existing, native_runner, mcp_runner + ) + + else: + self.completed_count += 1 + logger.info( + "Processing test case", + query=test_case.query[:50] + "...", + progress=self._get_progress_string(), + ) + # Run all approaches in parallel + tasks = [] + for approach in native_runner.APPROACHES: + tasks.append(native_runner.search_tool(test_case, approach)) + tasks.append(mcp_runner.search_tool(test_case)) + + results = await asyncio.gather(*tasks, return_exceptions=True) + + # Parse results - first ones are native (bm25, regex), last is mcp + native_results = {} + for i, approach in enumerate(native_runner.APPROACHES): + native_results[approach] = results[i] + mcp_result = results[-1] + + # Handle exceptions + native_results = self._handle_native_exceptions(native_results, test_case) + mcp_result = self._handle_mcp_exception(mcp_result, test_case) + + # Compute comparison result + return MetricsComputer.compute_single_result(test_case, native_results, mcp_result) + + async def _run_comparison( + self, + test_cases: list[TestCase], + native_runner: NativeApproachRunner, + mcp_runner: McpOptimizerAgentRunner, + existing_results: dict[str, "ComparisonResult"], + ) -> list[ComparisonResult]: + """Run comparison on all test cases with concurrency control. + + Args: + test_cases: List of test cases + native_runner: Native approach runner + mcp_runner: MCP Optimizer agent runner + existing_results: Dictionary of existing results to skip/retry + + Returns: + List of comparison results + """ + semaphore = asyncio.Semaphore(self.max_concurrency) + results_list: list[ComparisonResult] = [] + save_interval = 10 # Save partial results every N completions + + # Run all test cases with concurrent execution and periodic saves + async def process_and_track(): + """Process tasks and track results.""" + tasks = [ + self._run_single_test_case( + test_case, native_runner, mcp_runner, existing_results, semaphore + ) + for test_case in test_cases + ] + + # Use asyncio.as_completed to process results as they finish + for coro in asyncio.as_completed(tasks): + result = await coro + results_list.append(result) + + # Save partial results every save_interval completions + if len(results_list) % save_interval == 0: + self._save_partial_results(results_list) + + return results_list + + results = await process_and_track() + + # Final save to ensure all results are saved + self._save_partial_results(results) + + logger.info("Completed all test cases", count=len(results)) + return results diff --git a/examples/anthropic_comparison/download_data.sh b/examples/anthropic_comparison/download_data.sh new file mode 100755 index 0000000..41184bf --- /dev/null +++ b/examples/anthropic_comparison/download_data.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +# Script to download example data files from GitHub Release +# These files are excluded from git due to their size + +set -e + +REPO="StacklokLabs/mcp-optimizer" +RELEASE_TAG="anthropic-comparison-blogpost-data-v1.0.0" +BASE_URL="https://github.com/${REPO}/releases/download/${RELEASE_TAG}" + +FILES=( + "mcp_tools_cleaned.json" + "mcp_tools_cleaned_tests_claude-sonnet-4.json" + "results.json" + "results_accuracy_comparison.png" +) + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +echo "Downloading anthropic comparison example data files..." +echo "Target directory: ${SCRIPT_DIR}" +echo "" + +for file in "${FILES[@]}"; do + if [ -f "${SCRIPT_DIR}/${file}" ]; then + echo "⏭️ Skipping ${file} (already exists)" + else + echo "⬇️ Downloading ${file}..." + if curl -L -f -o "${SCRIPT_DIR}/${file}" "${BASE_URL}/${file}"; then + echo "✅ Downloaded ${file}" + else + echo "❌ Failed to download ${file}" + echo " URL: ${BASE_URL}/${file}" + exit 1 + fi + fi + echo "" +done + +echo "✨ All data files downloaded successfully!" +echo "" +echo "File sizes:" +du -sh "${SCRIPT_DIR}"/*.json "${SCRIPT_DIR}"/*.png 2>/dev/null || true diff --git a/examples/anthropic_comparison/ingest_test_data.py b/examples/anthropic_comparison/ingest_test_data.py new file mode 100644 index 0000000..d3c8e07 --- /dev/null +++ b/examples/anthropic_comparison/ingest_test_data.py @@ -0,0 +1,283 @@ +"""Standalone ingestion script for loading test data into MCP Optimizer database. + +This script loads tools from mcp_tools_cleaned.json and creates a testing database +that enables find_tool to work without a running ToolHive server. + +Uses core ingestion logic from IngestionService for consistency. +""" + +import asyncio +import json +import os +from pathlib import Path + +import structlog +from mcp.types import Tool + +from mcp_optimizer.db.config import DatabaseConfig +from mcp_optimizer.db.models import McpStatus, TransportType +from mcp_optimizer.db.workload_server_ops import WorkloadServerOps +from mcp_optimizer.db.workload_tool_ops import WorkloadToolOps +from mcp_optimizer.embeddings import EmbeddingManager +from mcp_optimizer.ingestion import IngestionService +from mcp_optimizer.token_counter import TokenCounter + +logger = structlog.get_logger(__name__) + + +class TestDataLoader: + """Loads test data using core ingestion logic for consistency.""" + + def __init__( + self, + ingestion_service: IngestionService, + workload_server_ops: WorkloadServerOps, + workload_tool_ops: WorkloadToolOps, + token_counter: TokenCounter, + ): + """Initialize with dependencies. + + Args: + ingestion_service: IngestionService instance (for _create_tool_text_to_embed) + workload_server_ops: WorkloadServerOps instance + workload_tool_ops: WorkloadToolOps instance + token_counter: TokenCounter instance + """ + self.ingestion_service = ingestion_service + self.workload_server_ops = workload_server_ops + self.workload_tool_ops = workload_tool_ops + self.token_counter = token_counter + # Get embedding_manager from ingestion_service for consistency + self.embedding_manager = ingestion_service.embedding_manager + + def _convert_tool_to_mcp_format(self, tool_dict: dict) -> Tool: + """Convert tool from mcp_tools_cleaned.json format to MCP Tool format. + + Args: + tool_dict: Tool dictionary from JSON + + Returns: + MCP Tool instance + """ + return Tool( + name=tool_dict["name"], + description=tool_dict.get("description", ""), + inputSchema={"properties": tool_dict.get("parameter", {})}, + ) + + async def load_from_json(self, json_path: Path) -> dict: + """Load tools from mcp_tools_cleaned.json using core ingestion logic. + + Uses the same logic as IngestionService._sync_workload_tools for consistency. + + Args: + json_path: Path to mcp_tools_cleaned.json + + Returns: + Dictionary with ingestion statistics + """ + logger.info("Loading test data", json_path=str(json_path)) + + # Load JSON + with open(json_path) as f: + servers_data = json.load(f) + + logger.info("Loaded servers from JSON", server_count=len(servers_data)) + + total_servers = 0 + total_tools = 0 + errors = [] + + # Get database connection and start transaction + async with self.workload_server_ops.db.begin_transaction() as conn: + for server_dict in servers_data: + server_name = server_dict["name"] + + try: + logger.info("Processing server", server_name=server_name) + + # Convert tools to MCP format + tools = [ + self._convert_tool_to_mcp_format(tool_dict) + for tool_dict in server_dict.get("tools", []) + ] + + if not tools: + logger.warning("No tools found for server", server_name=server_name) + continue + + # Create workload server + server = await self.workload_server_ops.create_server( + name=server_name, + url=f"http://test-{server_name.lower().replace(' ', '-')}.local", + workload_identifier=f"test-{server_name.lower().replace(' ', '-')}", + remote=False, + transport=TransportType.SSE, + status=McpStatus.RUNNING, + description=server_dict.get( + "description", f"Test server for {server_name}" + ), + conn=conn, + ) + server_id = server.id + + # Sync tools using same logic as IngestionService._sync_workload_tools + # Generate texts for all tools using the server name as context + # Use IngestionService._create_tool_text_to_embed for consistency + tool_texts = [ + self.ingestion_service._create_tool_text_to_embed(tool, server_name) + for tool in tools + ] + + # Generate embeddings for all tools at once + embeddings = self.embedding_manager.generate_embedding(tool_texts) + + # Calculate token counts for all tools + token_counts = [self.token_counter.count_tool_tokens(tool) for tool in tools] + + # Create tool records + create_tasks = [] + for tool, embedding, token_count in zip( + tools, embeddings, token_counts, strict=True + ): + task = self.workload_tool_ops.create_tool( + server_id=server_id, + details=tool, + details_embedding=embedding, + token_count=token_count, + conn=conn, + ) + create_tasks.append(task) + + await asyncio.gather(*create_tasks) + + total_servers += 1 + total_tools += len(tools) + + logger.info( + "Server processed successfully", + server_name=server_name, + tools_synced=len(tools), + ) + + except Exception as e: + logger.exception( + "Failed to process server", + server_name=server_name, + error=str(e), + ) + errors.append({"server": server_name, "error": str(e)}) + + # Sync vector tables after successful ingestion + # This populates the virtual vector tables used for semantic search + if total_servers > 0: + logger.info( + "Synchronizing vector tables after ingestion", + total_servers=total_servers, + total_tools=total_tools, + ) + await self.workload_tool_ops.sync_tool_vectors(conn=conn) + await self.workload_tool_ops.sync_tool_fts(conn=conn) + logger.info("Vector synchronization completed") + + return { + "total_servers": total_servers, + "total_tools": total_tools, + "errors": errors, + } + + +async def main(): + """Main entry point for standalone ingestion.""" + # Setup logging + structlog.configure( + wrapper_class=structlog.make_filtering_bound_logger(20), # INFO level + ) + + # Path to test data + json_path = Path(__file__).parent / Path("mcp_tools_cleaned.json") + + if not json_path.exists(): + raise FileNotFoundError(f"Test data not found: {json_path}") + + # Initialize database config (use test database in current directory) + test_db_path = Path("./mcp_optimizer_test.db").resolve() + + # Set environment variables for migrations + async_db_url = f"sqlite+aiosqlite:///{test_db_path}" + sync_db_url = f"sqlite:///{test_db_path}" + + os.environ["ASYNC_DB_URL"] = async_db_url + os.environ["DB_URL"] = sync_db_url + + db_config = DatabaseConfig(database_url=async_db_url) + + # Run migrations to create schema if needed + logger.info("Running database migrations", db_path=str(test_db_path)) + from mcp_optimizer.db.config import run_migrations + + try: + run_migrations() + logger.info("Migrations completed successfully") + except Exception as e: + logger.warning("Migration error (may be expected if schema exists)", error=str(e)) + + # Initialize embedding manager + logger.info("Initializing embedding manager") + embedding_manager = EmbeddingManager( + model_name="BAAI/bge-small-en-v1.5", + enable_cache=True, + threads=2, + fastembed_cache_path=None, + ) + + # Initialize token counter + token_counter = TokenCounter(encoding_name="cl100k_base") + + # Initialize ops classes + workload_server_ops = WorkloadServerOps(db_config) + workload_tool_ops = WorkloadToolOps(db_config) + + # Create IngestionService to reuse its _create_tool_text_to_embed method + ingestion_service = IngestionService( + db_config=db_config, + embedding_manager=embedding_manager, + mcp_timeout=10, + registry_ingestion_batch_size=5, + workload_ingestion_batch_size=5, + encoding="cl100k_base", + skipped_workloads=[], + runtime_mode="docker", + k8s_api_server_url="http://127.0.0.1:8001", + k8s_namespace=None, + k8s_all_namespaces=True, + ) + + # Create test data loader with dependencies + test_loader = TestDataLoader( + ingestion_service=ingestion_service, + workload_server_ops=workload_server_ops, + workload_tool_ops=workload_tool_ops, + token_counter=token_counter, + ) + + # Run ingestion + logger.info("Starting ingestion") + stats = await test_loader.load_from_json(json_path) + + # Print results + logger.info( + "Ingestion complete", + total_servers=stats["total_servers"], + total_tools=stats["total_tools"], + error_count=len(stats["errors"]), + ) + + if stats["errors"]: + logger.warning("Errors during ingestion", errors=stats["errors"]) + + return stats + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/anthropic_comparison/mcp_optimizer_agent.py b/examples/anthropic_comparison/mcp_optimizer_agent.py new file mode 100644 index 0000000..52b8f13 --- /dev/null +++ b/examples/anthropic_comparison/mcp_optimizer_agent.py @@ -0,0 +1,213 @@ +"""MCP Optimizer agent-based approach for comparison.""" + +import json +import os +import time +from pathlib import Path + +import structlog +from mcp.types import ListToolsResult +from models import ChosenMcpServerTool, McpOptimizerSearchResult, TestCase +from pydantic_ai import Agent +from pydantic_ai.agent import AgentRunResult +from pydantic_ai.messages import ModelRequest, ModelResponse, ToolCallPart, ToolReturnPart +from pydantic_ai.models.openai import OpenAIChatModel +from pydantic_ai.providers.openrouter import OpenRouterProvider + +from mcp_optimizer.config import MCPOptimizerConfig +from mcp_optimizer.db.config import DatabaseConfig +from mcp_optimizer.db.workload_tool_ops import WorkloadToolOps +from mcp_optimizer.embeddings import EmbeddingManager +from mcp_optimizer.server import find_tool + +logger = structlog.get_logger(__name__) + +SYSTEM_PROMPT = """You are a tool selection agent designed to identify the most appropriate tool +for solving user queries. Your primary function is to analyze user requests and recommend +the single best tool to address their needs. + +Instructions: +- Analyze the user's query to understand their specific need or problem +- Use the find_tool function exactly once to search for relevant tools +- Select the most appropriate tool from the results +- Respond with your recommendation +""" + + +class McpOptimizerAgentRunner: + """Runs comparison using agent with find_tool.""" + + def __init__( + self, + llm_model: str = "anthropic/claude-sonnet-4.5", + test_db_path: Path | None = None, + ): + """Initialize agent with find_tool as available tool. + + Args: + llm_model: OpenRouter model to use + test_db_path: Optional path to test database + (defaults to mcp_optimizer_test.db in current directory) + """ + openrouter_api_key = os.environ.get("OPENROUTER_API_KEY") + if not openrouter_api_key: + raise ValueError("OPENROUTER_API_KEY environment variable is required") + + # Initialize MCP Optimizer components needed for find_tool + # We only need: embedding_manager, _config, workload_tool_ops + if test_db_path is None: + # Default to test database in examples/anthropic_comparison directory + test_db_path = (Path(__file__).parent / "mcp_optimizer_test.db").resolve() + + async_db_url = f"sqlite+aiosqlite:///{test_db_path}" + + config = MCPOptimizerConfig( + async_db_url=async_db_url, + db_url=f"sqlite:///{test_db_path}", + ) + + # Initialize only what find_tool needs, avoiding ToolhiveClient initialization + import mcp_optimizer.server as server_module + + server_module._config = config + db = DatabaseConfig(database_url=config.async_db_url) + server_module.workload_tool_ops = WorkloadToolOps(db) + server_module.embedding_manager = EmbeddingManager( + model_name=config.embedding_model_name, + enable_cache=config.enable_embedding_cache, + threads=config.embedding_threads, + fastembed_cache_path=config.fastembed_cache_path, + ) + + logger.info( + "Initialized MCP Optimizer components for find_tool", + db_path=str(test_db_path), + ) + + # Create agent with find_tool + self.agent: Agent[None, ChosenMcpServerTool] = Agent( + model=OpenAIChatModel( + llm_model, provider=OpenRouterProvider(api_key=openrouter_api_key) + ), + system_prompt=SYSTEM_PROMPT, + tools=[find_tool], + retries=2, + output_retries=2, + output_type=ChosenMcpServerTool, + ) + + async def search_tool(self, test_case: TestCase) -> McpOptimizerSearchResult: + """Execute search using agent with find_tool. + + Args: + test_case: Test case with query + + Returns: + McpOptimizerSearchResult with selected tool and metrics + """ + start_time = time.perf_counter() + + try: + # Run agent with test query + result = await self.agent.run(test_case.query) + + search_time = time.perf_counter() - start_time + + # Extract tool calls and results from messages + tools_retrieved, made_queries = self._get_tool_calls(result) + + # Parse retrieved tools + retrieved_tools = self._parse_tools(tools_retrieved) + + # Get token usage + usage = result.usage() + + actual_output = ChosenMcpServerTool.model_validate(result.output) + + return McpOptimizerSearchResult( + test_case=test_case, + selected_server_name=actual_output.server_name, + selected_tool_name=actual_output.tool_name, + retrieved_tools=retrieved_tools, + search_time_s=search_time, + request_tokens=usage.input_tokens, + response_tokens=usage.output_tokens, + total_tokens=usage.input_tokens + usage.output_tokens, + num_tools_returned=len(retrieved_tools), + ) + + except Exception as e: + logger.exception( + "MCP Optimizer agent search failed", query=test_case.query, error=str(e) + ) + return McpOptimizerSearchResult( + test_case=test_case, + selected_server_name=None, + selected_tool_name=None, + retrieved_tools=[], + search_time_s=time.perf_counter() - start_time, + request_tokens=0, + response_tokens=0, + total_tokens=0, + num_tools_returned=0, + error=str(e), + ) + + def _get_tool_calls( + self, agent_response: AgentRunResult + ) -> tuple[list[ListToolsResult], list[dict]]: + """Extract tool calls from agent messages. + + Following agent_evaluation.py pattern: + - Walk through agent_response.all_messages() + - Find ModelResponse with ToolCallPart for find_tool + - Find ModelRequest with ToolReturnPart with results + + Args: + agent_response: Agent run result + + Returns: + Tuple of (tools_retrieved, made_queries) + """ + current_query = None + tools_retrieved = [] + made_queries = [] + + for message in agent_response.all_messages(): + if isinstance(message, ModelResponse): + for part in message.parts: + if isinstance(part, ToolCallPart) and part.tool_name == "find_tool": + current_query = json.loads(part.args) + made_queries.append(current_query) + elif isinstance(message, ModelRequest): + for part in message.parts: + if isinstance(part, ToolReturnPart) and part.tool_name == "find_tool": + if current_query is None: + logger.warning("Find query in response but no find_tool call found") + continue + tools_response = ListToolsResult.model_validate(part.content) + tools_retrieved.append(tools_response) + current_query = None + + return tools_retrieved, made_queries + + def _parse_tools(self, tools_in_response: list[ListToolsResult]) -> list[tuple[str, str]]: + """Parse tools from ListToolsResult. + + Args: + tools_in_response: List of ListToolsResult from find_tool + + Returns: + List of (server_name, tool_name) tuples + """ + if not tools_in_response: + return [] + elif len(tools_in_response) > 1: + logger.warning( + "Multiple find_tool responses found, using first", + count=len(tools_in_response), + ) + + # Use first response + tools_result = tools_in_response[0] + return [(tool.mcp_server_name, tool.name) for tool in tools_result.tools] diff --git a/examples/anthropic_comparison/metrics.py b/examples/anthropic_comparison/metrics.py new file mode 100644 index 0000000..f7d6541 --- /dev/null +++ b/examples/anthropic_comparison/metrics.py @@ -0,0 +1,405 @@ +"""Metrics computation for comparison evaluation.""" + +import json +from pathlib import Path + +import click +import structlog +from models import ( + AggregateMetrics, + ComparisonReport, + ComparisonResult, + McpOptimizerSearchResult, + NativeSearchResult, + TestCase, +) +from results_exporter import ResultsExporter + +logger = structlog.get_logger(__name__) + + +class MetricsComputer: + """Computes metrics for comparison results.""" + + @staticmethod + def compute_single_result( + test_case: TestCase, + native_results: dict[str, NativeSearchResult], + mcp_result: McpOptimizerSearchResult, + ) -> ComparisonResult: + """Compute metrics for a single test case. + + Args: + test_case: Test case with expected tool + native_results: Results from native approaches, keyed by approach + mcp_result: Result from MCP Optimizer approach + + Returns: + ComparisonResult with computed metrics + """ + # Expected tool name format is {server}-{tool}, e.g. "agentql-extract-web-data" + expected_native_name = ( + f"{test_case.target_mcp_server_name.lower()}-{test_case.target_tool_name}" + ) + + # Compute metrics for each native approach + native_is_correct = {} + native_precision_at_3 = {} + native_precision_at_5 = {} + native_precision_at_max = {} + native_tool_position = {} + + for approach, native_result in native_results.items(): + # Check if selected tool matches target + is_correct = ( + native_result.selected_tool_name is not None + and native_result.selected_tool_name == expected_native_name + ) + native_is_correct[approach] = is_correct + + # Find tool position in retrieved tools list + tool_position = None + for idx, tool_name in enumerate(native_result.retrieved_tools): + if tool_name == expected_native_name: + tool_position = idx + break + + native_tool_position[approach] = tool_position + + # Precision@3: 1.0 if target tool in first 3 retrieved tools, 0.0 otherwise + native_precision_at_3[approach] = ( + 1.0 if tool_position is not None and tool_position < 3 else 0.0 + ) + + # Precision@5: 1.0 if target tool in first 5 retrieved tools, 0.0 otherwise + native_precision_at_5[approach] = ( + 1.0 if tool_position is not None and tool_position < 5 else 0.0 + ) + + # Precision@max: 1.0 if target tool found at any position, 0.0 otherwise + native_precision_at_max[approach] = 1.0 if tool_position is not None else 0.0 + + # MCP Optimizer: Check if selected tool matches target + mcp_optimizer_is_correct = ( + mcp_result.selected_server_name == test_case.target_mcp_server_name + and mcp_result.selected_tool_name == test_case.target_tool_name + ) + + # MCP Optimizer: Find tool position and compute precision + mcp_optimizer_tool_position = None + for idx, (server, tool) in enumerate(mcp_result.retrieved_tools): + if server == test_case.target_mcp_server_name and tool == test_case.target_tool_name: + mcp_optimizer_tool_position = idx + break + + # Precision@3: 1.0 if target tool in first 3 retrieved tools, 0.0 otherwise + mcp_optimizer_precision_at_3 = ( + 1.0 + if mcp_optimizer_tool_position is not None and mcp_optimizer_tool_position < 3 + else 0.0 + ) + + # Precision@5: 1.0 if target tool in first 5 retrieved tools, 0.0 otherwise + mcp_optimizer_precision_at_5 = ( + 1.0 + if mcp_optimizer_tool_position is not None and mcp_optimizer_tool_position < 5 + else 0.0 + ) + + # Precision@max: 1.0 if target tool found at any position, 0.0 otherwise + mcp_optimizer_precision_at_max = 1.0 if mcp_optimizer_tool_position is not None else 0.0 + + return ComparisonResult( + test_case=test_case, + native_results=native_results, + mcp_optimizer_result=mcp_result, + native_is_correct=native_is_correct, + mcp_optimizer_is_correct=mcp_optimizer_is_correct, + native_precision_at_3=native_precision_at_3, + native_precision_at_5=native_precision_at_5, + native_precision_at_max=native_precision_at_max, + mcp_optimizer_precision_at_3=mcp_optimizer_precision_at_3, + mcp_optimizer_precision_at_5=mcp_optimizer_precision_at_5, + mcp_optimizer_precision_at_max=mcp_optimizer_precision_at_max, + native_tool_position=native_tool_position, + mcp_optimizer_tool_position=mcp_optimizer_tool_position, + ) + + @staticmethod + def compute_aggregate_metrics( # noqa: C901 + results: list[ComparisonResult], + ) -> AggregateMetrics: + """Compute aggregate metrics across all results. + + Args: + results: List of comparison results + + Returns: + AggregateMetrics with summary statistics + """ + if not results: + raise ValueError("No results to compute metrics") + + total_cases = len(results) + + # Get all approaches from the first result + approaches = list(results[0].native_results.keys()) + + # Count successes per approach + native_success_count = {} + for approach in approaches: + native_success_count[approach] = sum( + 1 for r in results if r.native_is_correct[approach] + ) + + mcp_optimizer_success_count = sum(1 for r in results if r.mcp_optimizer_is_correct) + + # Accuracy (exact match rate) per approach + native_accuracy = {} + for approach in approaches: + native_accuracy[approach] = native_success_count[approach] / total_cases + + mcp_optimizer_accuracy = mcp_optimizer_success_count / total_cases + + # Precision@3 (target tool in top 3 results rate) per approach + native_precision_at_3 = {} + for approach in approaches: + native_precision_at_3[approach] = ( + sum(r.native_precision_at_3[approach] for r in results) / total_cases + ) + + mcp_optimizer_precision_at_3 = ( + sum(r.mcp_optimizer_precision_at_3 for r in results) / total_cases + ) + + # Precision@5 (target tool in top 5 results rate) per approach + native_precision_at_5 = {} + for approach in approaches: + native_precision_at_5[approach] = ( + sum(r.native_precision_at_5[approach] for r in results) / total_cases + ) + + mcp_optimizer_precision_at_5 = ( + sum(r.mcp_optimizer_precision_at_5 for r in results) / total_cases + ) + + # Precision@max (target tool found at any position rate) per approach + native_precision_at_max = {} + for approach in approaches: + native_precision_at_max[approach] = ( + sum(r.native_precision_at_max[approach] for r in results) / total_cases + ) + + mcp_optimizer_precision_at_max = ( + sum(r.mcp_optimizer_precision_at_max for r in results) / total_cases + ) + + # Average position (only for cases where tool was found) per approach + native_avg_position = {} + for approach in approaches: + positions = [ + r.native_tool_position[approach] + for r in results + if r.native_tool_position[approach] is not None + ] + native_avg_position[approach] = sum(positions) / len(positions) if positions else 0.0 + + mcp_optimizer_positions = [ + r.mcp_optimizer_tool_position + for r in results + if r.mcp_optimizer_tool_position is not None + ] + mcp_optimizer_avg_position = ( + sum(mcp_optimizer_positions) / len(mcp_optimizer_positions) + if mcp_optimizer_positions + else 0.0 + ) + + # Search time per approach + native_avg_search_time_s = {} + for approach in approaches: + native_avg_search_time_s[approach] = ( + sum(r.native_results[approach].search_time_s for r in results) / total_cases + ) + + mcp_optimizer_avg_search_time_s = ( + sum(r.mcp_optimizer_result.search_time_s for r in results) / total_cases + ) + + # Token usage per approach + native_avg_request_tokens = {} + native_avg_response_tokens = {} + native_avg_total_tokens = {} + for approach in approaches: + native_avg_request_tokens[approach] = ( + sum(r.native_results[approach].request_tokens for r in results) / total_cases + ) + native_avg_response_tokens[approach] = ( + sum(r.native_results[approach].response_tokens for r in results) / total_cases + ) + native_avg_total_tokens[approach] = ( + sum(r.native_results[approach].total_tokens for r in results) / total_cases + ) + + mcp_optimizer_avg_request_tokens = ( + sum(r.mcp_optimizer_result.request_tokens for r in results) / total_cases + ) + mcp_optimizer_avg_response_tokens = ( + sum(r.mcp_optimizer_result.response_tokens for r in results) / total_cases + ) + mcp_optimizer_avg_total_tokens = ( + sum(r.mcp_optimizer_result.total_tokens for r in results) / total_cases + ) + + # Error rates per approach + native_error_rate = {} + for approach in approaches: + error_count = sum(1 for r in results if r.native_results[approach].error is not None) + native_error_rate[approach] = error_count / total_cases + + mcp_optimizer_error_count = sum( + 1 for r in results if r.mcp_optimizer_result.error is not None + ) + mcp_optimizer_error_rate = mcp_optimizer_error_count / total_cases + + # Average number of tools retrieved per approach + native_avg_tools_retrieved = {} + for approach in approaches: + native_avg_tools_retrieved[approach] = ( + sum(r.native_results[approach].num_tools_returned for r in results) / total_cases + ) + + mcp_optimizer_avg_tools_retrieved = ( + sum(r.mcp_optimizer_result.num_tools_returned for r in results) / total_cases + ) + + logger.info( + "Computed aggregate metrics", + total_cases=total_cases, + native_accuracy={k: f"{v:.2%}" for k, v in native_accuracy.items()}, + mcp_optimizer_accuracy=f"{mcp_optimizer_accuracy:.2%}", + ) + + return AggregateMetrics( + total_cases=total_cases, + native_success_count=native_success_count, + mcp_optimizer_success_count=mcp_optimizer_success_count, + native_accuracy=native_accuracy, + mcp_optimizer_accuracy=mcp_optimizer_accuracy, + native_precision_at_3=native_precision_at_3, + native_precision_at_5=native_precision_at_5, + native_precision_at_max=native_precision_at_max, + mcp_optimizer_precision_at_3=mcp_optimizer_precision_at_3, + mcp_optimizer_precision_at_5=mcp_optimizer_precision_at_5, + mcp_optimizer_precision_at_max=mcp_optimizer_precision_at_max, + native_avg_position=native_avg_position, + mcp_optimizer_avg_position=mcp_optimizer_avg_position, + native_avg_search_time_s=native_avg_search_time_s, + mcp_optimizer_avg_search_time_s=mcp_optimizer_avg_search_time_s, + native_avg_request_tokens=native_avg_request_tokens, + native_avg_response_tokens=native_avg_response_tokens, + native_avg_total_tokens=native_avg_total_tokens, + mcp_optimizer_avg_request_tokens=mcp_optimizer_avg_request_tokens, + mcp_optimizer_avg_response_tokens=mcp_optimizer_avg_response_tokens, + mcp_optimizer_avg_total_tokens=mcp_optimizer_avg_total_tokens, + native_error_rate=native_error_rate, + mcp_optimizer_error_rate=mcp_optimizer_error_rate, + native_avg_tools_retrieved=native_avg_tools_retrieved, + mcp_optimizer_avg_tools_retrieved=mcp_optimizer_avg_tools_retrieved, + ) + + +@click.command() +@click.option( + "--input-file", + type=click.Path(exists=True, path_type=Path), + required=True, + help="Path to comparison results JSON file", +) +@click.option( + "--output-file", + type=click.Path(path_type=Path), + default=None, + help="Path to save updated results (defaults to same as input)", +) +def recalculate_metrics(input_file: Path, output_file: Path | None) -> None: + """Recalculate aggregate metrics from a comparison results JSON file. + + This is useful when the metrics computation logic has been updated + and you want to recalculate metrics for existing results. + + Args: + input_file: Path to comparison results JSON file + output_file: Optional path to save updated results (defaults to input_file) + """ + # Default output to input if not specified + if output_file is None: + output_file = input_file + + logger.info("Loading comparison report", input_file=str(input_file)) + + # Load the comparison report from JSON + with open(input_file) as f: + data = json.load(f) + + # Extract individual results - we'll recalculate metrics from raw data + raw_results = data["individual_results"] + + logger.info( + "Loaded report", + num_cases=len(raw_results), + dataset=data.get("dataset_path", "unknown"), + ) + + # Recalculate individual result metrics to ensure new fields are populated + logger.info("Recalculating individual result metrics") + individual_results = [] + for result_data in raw_results: + # Extract the core data needed for recalculation + test_case = TestCase.model_validate(result_data["test_case"]) + native_results = { + approach: NativeSearchResult.model_validate(native_data) + for approach, native_data in result_data["native_results"].items() + } + mcp_result = McpOptimizerSearchResult.model_validate(result_data["mcp_optimizer_result"]) + + # Recalculate all metrics for this result + recalculated_result = MetricsComputer.compute_single_result( + test_case=test_case, + native_results=native_results, + mcp_result=mcp_result, + ) + individual_results.append(recalculated_result) + + # Recalculate aggregate metrics from individual results + logger.info("Recalculating aggregate metrics") + new_metrics = MetricsComputer.compute_aggregate_metrics(individual_results) + + # Create updated report with new metrics + report = ComparisonReport( + aggregate_metrics=new_metrics, + individual_results=individual_results, + dataset_path=data.get("dataset_path", "unknown"), + num_test_cases=len(individual_results), + timestamp=data.get("timestamp", "unknown"), + native_model=data.get("native_model", "claude-sonnet-4-5-20250929"), + mcp_optimizer_model=data.get("mcp_optimizer_model", "anthropic/claude-sonnet-4"), + ) + + # Print summary to console + ResultsExporter.print_console_summary(report) + + # Save updated report + ResultsExporter.save_json_report(report, output_file) + + # Generate and save accuracy comparison plot + plot_file = output_file.parent / f"{output_file.stem}_accuracy_comparison.png" + ResultsExporter.save_accuracy_comparison_plot(report, plot_file) + + logger.info("Updated metrics saved", output_file=str(output_file)) + print(f"\n✓ Updated results saved to {output_file}") + print(f"✓ Accuracy comparison plot saved to {plot_file}") + + +if __name__ == "__main__": + recalculate_metrics() diff --git a/examples/anthropic_comparison/models.py b/examples/anthropic_comparison/models.py new file mode 100644 index 0000000..906d037 --- /dev/null +++ b/examples/anthropic_comparison/models.py @@ -0,0 +1,233 @@ +"""Pydantic models for MCP Optimizer vs Anthropic Native comparison.""" + +import json +from pathlib import Path + +from pydantic import BaseModel, Field + + +class TestCase(BaseModel): + """Test case from dataset.""" + + query: str = Field(description="User query") + target_mcp_server_name: str = Field(description="Expected server name") + target_tool_name: str = Field(description="Expected tool name") + keywords: str = Field(description="Keywords for BM25 search") + predicted_server_name: str | None = Field(default=None, description="LLM-predicted server name") + generated_tool_description: str | None = Field( + default=None, description="LLM-generated tool description" + ) + generated_server_description: str | None = Field( + default=None, description="LLM-generated server description" + ) + + +class TestDataset(BaseModel): + """Collection of test cases.""" + + cases: list[TestCase] + + @classmethod + def load_from_json(cls, path: Path, limit: int | None = None) -> "TestDataset": + """Load test cases from JSON file. + + Args: + path: Path to test dataset JSON + limit: Optional limit on number of cases + + Returns: + TestDataset instance + """ + with open(path) as f: + data = json.load(f) + + cases = [TestCase.model_validate(item) for item in data] + + if limit: + cases = cases[:limit] + + return cls(cases=cases) + + +class ChosenMcpServerTool(BaseModel): + """Tool chosen by agent.""" + + server_name: str = Field(description="MCP server name") + tool_name: str = Field(description="Tool name") + + +class NativeSearchResult(BaseModel): + """Result from Anthropic native search.""" + + test_case: TestCase + approach: str = Field(description="Search approach used: bm25, regex, or both") + selected_tool_name: str | None = Field( + default=None, description="Name of selected tool in format {server}-{tool}" + ) + retrieved_tools: list[str] = Field( + default_factory=list, + description="Tools returned by tool search as tool names", + ) + num_tools_returned: int = Field( + default=0, description="Number of tools returned by tool search" + ) + search_time_s: float = Field(default=0.0, description="Search time in seconds") + request_tokens: int = Field(default=0, description="Request tokens used") + response_tokens: int = Field(default=0, description="Response tokens generated") + total_tokens: int = Field(default=0, description="Total tokens used") + error: str | None = Field(default=None, description="Error message if failed") + + +class McpOptimizerSearchResult(BaseModel): + """Result from MCP Optimizer agent search.""" + + test_case: TestCase + selected_server_name: str | None = Field(default=None, description="Selected MCP server name") + selected_tool_name: str | None = Field(default=None, description="Selected tool name") + retrieved_tools: list[tuple[str, str]] = Field( + default_factory=list, + description="Tools returned by find_tool as (server, tool) tuples", + ) + search_time_s: float = Field(default=0.0, description="Search time in seconds") + request_tokens: int = Field(default=0, description="Request tokens used") + response_tokens: int = Field(default=0, description="Response tokens generated") + total_tokens: int = Field(default=0, description="Total tokens used") + num_tools_returned: int = Field(default=0, description="Number of tools returned by find_tool") + error: str | None = Field(default=None, description="Error message if failed") + + +class ComparisonResult(BaseModel): + """Combined result comparing both approaches.""" + + test_case: TestCase + native_results: dict[str, NativeSearchResult] = Field( + description="Native results keyed by approach (bm25, regex, both)" + ) + mcp_optimizer_result: McpOptimizerSearchResult + + # Computed metrics (now keyed by approach) + native_is_correct: dict[str, bool] = Field( + description="Whether native approach selected correct tool, by approach" + ) + mcp_optimizer_is_correct: bool = Field( + description="Whether MCP Optimizer selected correct tool" + ) + native_precision_at_3: dict[str, float] = Field( + description="1.0 if target tool in first 3 retrieved tools, 0.0 otherwise, by approach" + ) + native_precision_at_5: dict[str, float] = Field( + description="1.0 if target tool in first 5 retrieved tools, 0.0 otherwise, by approach" + ) + native_precision_at_max: dict[str, float] = Field( + default_factory=dict, + description="1.0 if tool found at any position, 0.0 otherwise, by approach", + ) + mcp_optimizer_precision_at_3: float = Field( + description="1.0 if target tool in first 3 retrieved tools, 0.0 otherwise" + ) + mcp_optimizer_precision_at_5: float = Field( + description="1.0 if target tool in first 5 retrieved tools, 0.0 otherwise" + ) + mcp_optimizer_precision_at_max: float = Field( + default=0.0, + description="1.0 if target tool found in retrieved tools at any position, 0.0 otherwise", + ) + native_tool_position: dict[str, int | None] = Field( + description=( + "0-based position of target tool in retrieved results (None if not found), by approach" + ) + ) + mcp_optimizer_tool_position: int | None = Field( + default=None, + description="0-based position of target tool in retrieved results (None if not found)", + ) + + +class AggregateMetrics(BaseModel): + """Aggregate metrics across all test cases.""" + + total_cases: int = Field(description="Total number of test cases") + native_success_count: dict[str, int] = Field( + description="Number of correct selections by native approach, by approach" + ) + mcp_optimizer_success_count: int = Field( + description="Number of correct selections by MCP Optimizer" + ) + native_accuracy: dict[str, float] = Field( + description="Accuracy rate for native approach (0.0-1.0), by approach" + ) + mcp_optimizer_accuracy: float = Field(description="Accuracy rate for MCP Optimizer (0.0-1.0)") + native_precision_at_3: dict[str, float] = Field( + description="Precision@3 for native approach (0.0-1.0), by approach" + ) + native_precision_at_5: dict[str, float] = Field( + description="Precision@5 for native approach (0.0-1.0), by approach" + ) + native_precision_at_max: dict[str, float] = Field( + description="Precision@max for native approach (0.0-1.0), by approach" + ) + mcp_optimizer_precision_at_3: float = Field( + description="Precision@3 for MCP Optimizer (0.0-1.0)" + ) + mcp_optimizer_precision_at_5: float = Field( + description="Precision@5 for MCP Optimizer (0.0-1.0)" + ) + mcp_optimizer_precision_at_max: float = Field( + description="Precision@max for MCP Optimizer (0.0-1.0)" + ) + native_avg_position: dict[str, float] = Field( + description="Average position of target tool (when found), by approach" + ) + mcp_optimizer_avg_position: float = Field( + description="Average position of target tool (when found)" + ) + native_avg_search_time_s: dict[str, float] = Field( + description="Average search time in seconds, by approach" + ) + mcp_optimizer_avg_search_time_s: float = Field(description="Average search time in seconds") + native_avg_request_tokens: dict[str, float] = Field( + description="Average request tokens used per query, by approach" + ) + native_avg_response_tokens: dict[str, float] = Field( + description="Average response tokens used per query, by approach" + ) + native_avg_total_tokens: dict[str, float] = Field( + description="Average total tokens used per query, by approach" + ) + mcp_optimizer_avg_request_tokens: float = Field( + description="Average request tokens used per query" + ) + mcp_optimizer_avg_response_tokens: float = Field( + description="Average response tokens used per query" + ) + mcp_optimizer_avg_total_tokens: float = Field(description="Average total tokens used per query") + native_error_rate: dict[str, float] = Field( + description="Error rate for native approach (0.0-1.0), by approach" + ) + mcp_optimizer_error_rate: float = Field(description="Error rate for MCP Optimizer (0.0-1.0)") + native_avg_tools_retrieved: dict[str, float] = Field( + description="Average number of tools retrieved per query, by approach" + ) + mcp_optimizer_avg_tools_retrieved: float = Field( + description="Average number of tools retrieved per query" + ) + + +class ComparisonReport(BaseModel): + """Full comparison report.""" + + aggregate_metrics: AggregateMetrics + individual_results: list[ComparisonResult] + dataset_path: str = Field(description="Path to test dataset") + num_test_cases: int = Field(description="Number of test cases evaluated") + timestamp: str = Field(description="Timestamp of evaluation (ISO format)") + native_model: str = Field(default="claude-sonnet-4-5-20250929", description="Native model used") + mcp_optimizer_model: str = Field( + default="anthropic/claude-sonnet-4", description="MCP Optimizer model used" + ) + baseline_all_tools_tokens: int | None = Field( + default=None, + description=( + "Total tokens used when loading all tools without defer, betas, or search tools" + ), + ) diff --git a/examples/anthropic_comparison/native_approach.py b/examples/anthropic_comparison/native_approach.py new file mode 100644 index 0000000..da6c43b --- /dev/null +++ b/examples/anthropic_comparison/native_approach.py @@ -0,0 +1,243 @@ +"""Native Anthropic search approaches for comparison.""" + +import asyncio +import os +import time + +import structlog +from anthropic import AsyncAnthropic +from models import NativeSearchResult, TestCase + +logger = structlog.get_logger(__name__) + + +class NativeApproachRunner: + """Runs comparison using Anthropic's native search approaches.""" + + APPROACHES = ["bm25", "regex"] + + def __init__(self, api_key: str, all_tools: list[dict]): + """Initialize with Anthropic API key and converted tools. + + Args: + api_key: Anthropic API key + all_tools: List of tools in Anthropic format (from ToolConverter) + """ + self.client = AsyncAnthropic(api_key=api_key) + self.all_tools = all_tools + self.bm25_search_tool = [ + {"type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25"} + ] + self.regex_search_tool = [ + {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"} + ] + + async def measure_baseline_all_tools_tokens( + self, test_query: str = "List available tools" + ) -> int: + """Measure total tokens when loading all tools without defer, betas, or search tools. + + This provides a baseline for comparison - the token cost when all tools are loaded + upfront without any search mechanism. + + Args: + test_query: Simple query to use for the test + + Returns: + Total tokens used (input + output) + """ + try: + baseline_tools = [ + { + "name": tool.get("name"), + "description": tool.get("description"), + "input_schema": tool.get("input_schema"), + } + for tool in self.all_tools + ] + # Call Claude with all tools, no betas, no search tools + message = await self.client.messages.create( + model="claude-sonnet-4-5-20250929", + max_tokens=2048, + tools=baseline_tools, + messages=[{"role": "user", "content": test_query}], + ) + + total_tokens = message.usage.input_tokens + message.usage.output_tokens + logger.info( + "Baseline all tools token measurement", + input_tokens=message.usage.input_tokens, + output_tokens=message.usage.output_tokens, + total_tokens=total_tokens, + num_tools=len(baseline_tools), + ) + return total_tokens + + except Exception as e: + logger.exception("Failed to measure baseline all tools tokens", error=str(e)) + return 0 + + async def search_tool(self, test_case: TestCase, approach: str = "bm25") -> NativeSearchResult: + """Execute search using Anthropic native approach. + + Args: + test_case: Test case with query + approach: Search approach to use (bm25, regex, or both) + + Returns: + NativeSearchResult with selected tool and metrics + """ + if approach not in self.APPROACHES: + raise ValueError(f"Invalid approach: {approach}. Must be one of {self.APPROACHES}") + + start_time = time.perf_counter() + + try: + # Determine which search tools to include + approach_config = { + "bm25": (self.bm25_search_tool, {"tool_search_tool_bm25"}), + "regex": (self.regex_search_tool, {"tool_search_tool_regex"}), + } + + if approach not in approach_config: + raise ValueError(f"Unsupported approach: {approach}") + + search_tool, skip_tool_names = approach_config[approach] + + # Call Claude with search tool(s) + all tools + message = await self.client.beta.messages.create( + model="claude-sonnet-4-5-20250929", + betas=["advanced-tool-use-2025-11-20"], + max_tokens=2048, + tools=search_tool + self.all_tools, + messages=[{"role": "user", "content": test_case.query}], + ) + + search_time = time.perf_counter() - start_time + + # Extract tool uses and search results from content + selected_tool = None + retrieved_tools = [] + + for block in message.content: + # Extract retrieved tools from tool_search_tool_result blocks + if hasattr(block, "type") and block.type == "tool_search_tool_result": + if hasattr(block, "content") and hasattr(block.content, "tool_references"): + retrieved_tools = [ref.tool_name for ref in block.content.tool_references] + + # Extract selected tool from tool_use blocks + if hasattr(block, "type") and block.type == "tool_use": + # Skip the search tools themselves + if block.name not in skip_tool_names: + selected_tool = block.name + break + + return NativeSearchResult( + test_case=test_case, + approach=approach, + selected_tool_name=selected_tool, + retrieved_tools=retrieved_tools, + num_tools_returned=len(retrieved_tools), + search_time_s=search_time, + request_tokens=message.usage.input_tokens, + response_tokens=message.usage.output_tokens, + total_tokens=message.usage.input_tokens + message.usage.output_tokens, + ) + + except Exception as e: + logger.exception( + "Native search failed", query=test_case.query, approach=approach, error=str(e) + ) + return NativeSearchResult( + test_case=test_case, + approach=approach, + selected_tool_name=None, + retrieved_tools=[], + num_tools_returned=0, + search_time_s=time.perf_counter() - start_time, + request_tokens=0, + response_tokens=0, + total_tokens=0, + error=str(e), + ) + + +async def main(): + """Debug entrypoint with hardcoded values.""" + import json + from pathlib import Path + + from tool_converter import ToolConverter + + # Get API key from environment + api_key = os.environ.get("ANTHROPIC_API_KEY") + if not api_key: + raise ValueError("ANTHROPIC_API_KEY environment variable not set") + + # Define paths to the data files + script_dir = Path(__file__).parent + tools_file = script_dir / "mcp_tools_cleaned.json" + tests_file = script_dir / "mcp_tools_cleaned_tests_claude-sonnet-4.json" + + # Load and convert tools (same logic as in comparison_orchestrator.py) + logger.info("Loading tools from", path=str(tools_file)) + with open(tools_file) as f: + mcp_servers = json.load(f) + + # Convert all tools using shared ToolConverter + converter = ToolConverter() + all_tools = [] + + for server in mcp_servers: + for tool in server["tools"]: + anthropic_tool = converter.convert_tool(server, tool) + all_tools.append(anthropic_tool.model_dump()) + + logger.info("Converted tools", count=len(all_tools)) + + # Load test cases + logger.info("Loading test cases from", path=str(tests_file)) + with open(tests_file) as f: + test_data = json.load(f) + + # Use first test case for debugging + test_case_data = test_data[0] + test_case = TestCase( + query=test_case_data["query"], + target_mcp_server_name=test_case_data["target_mcp_server_name"], + target_tool_name=test_case_data["target_tool_name"], + keywords=test_case_data["keywords"], + predicted_server_name=test_case_data.get("predicted_server_name"), + generated_tool_description=test_case_data.get("generated_tool_description"), + generated_server_description=test_case_data.get("generated_server_description"), + ) + + logger.info("Using test case", query=test_case.query, target=test_case.target_tool_name) + + # Initialize runner + runner = NativeApproachRunner(api_key=api_key, all_tools=all_tools) + + # Run search with different approaches + print("\n" + "=" * 80) + print(f"Query: {test_case.query}") + print(f"Target: {test_case.target_mcp_server_name}/{test_case.target_tool_name}") + print("=" * 80) + + print("\nTesting BM25 approach...") + result_bm25 = await runner.search_tool(test_case, approach="bm25") + print(f"Selected tool: {result_bm25.selected_tool_name}") + print(f"Retrieved tools: {result_bm25.retrieved_tools}") + expected_tool = f"{test_case.target_mcp_server_name.lower()}-{test_case.target_tool_name}" + print(f"Correct: {result_bm25.selected_tool_name == expected_tool}") + print(f"Time: {result_bm25.search_time_s:.2f}s, Tokens: {result_bm25.total_tokens}") + + print("\nTesting Regex approach...") + result_regex = await runner.search_tool(test_case, approach="regex") + print(f"Selected tool: {result_regex.selected_tool_name}") + print(f"Retrieved tools: {result_regex.retrieved_tools}") + print(f"Correct: {result_regex.selected_tool_name == expected_tool}") + print(f"Time: {result_regex.search_time_s:.2f}s, Tokens: {result_regex.total_tokens}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/anthropic_comparison/results_exporter.py b/examples/anthropic_comparison/results_exporter.py new file mode 100644 index 0000000..13ab423 --- /dev/null +++ b/examples/anthropic_comparison/results_exporter.py @@ -0,0 +1,454 @@ +"""Utility functions for exporting comparison results.""" + +import csv +import json +from pathlib import Path + +import matplotlib.pyplot as plt +import structlog +from models import ComparisonReport +from rich.console import Console +from rich.panel import Panel +from rich.table import Table + +logger = structlog.get_logger(__name__) + + +class ResultsExporter: + """Export comparison results to multiple formats.""" + + @staticmethod + def print_console_summary(report: ComparisonReport) -> None: # noqa: C901 + """Print formatted summary to console using Rich. + + Args: + report: Comparison report to display + """ + console = Console() + metrics = report.aggregate_metrics + + # Header + console.print("\n") + console.print( + Panel.fit( + "[bold blue]MCP Optimizer vs Anthropic Native Comparison[/bold blue]", + border_style="blue", + ) + ) + + # Get all approaches + approaches = list(metrics.native_accuracy.keys()) + + # Summary table - one row per approach + summary_table = Table(title="Summary", show_header=True) + summary_table.add_column("Metric", style="cyan") + summary_table.add_column("Approach", style="yellow") + summary_table.add_column("Native", justify="right", style="magenta") + summary_table.add_column("MCP Optimizer", justify="right", style="green") + summary_table.add_column("Difference", justify="right") + + # Accuracy + for i, approach in enumerate(approaches): + mcp_text = f"{metrics.mcp_optimizer_accuracy:.2%}" if i == 0 else "" # Only show once + summary_table.add_row( + "Accuracy" if i == 0 else "", + approach, + f"{metrics.native_accuracy[approach]:.2%}", + mcp_text, + f"{(metrics.mcp_optimizer_accuracy - metrics.native_accuracy[approach]):.2%}", + ) + + # Precision@3 + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_precision_at_3:.2%}" if i == 0 else "" + ) # Only show once + diff = metrics.mcp_optimizer_precision_at_3 - metrics.native_precision_at_3[approach] + summary_table.add_row( + "Precision@3" if i == 0 else "", + approach, + f"{metrics.native_precision_at_3[approach]:.2%}", + mcp_text, + f"{diff:.2%}", + ) + + # Precision@5 + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_precision_at_5:.2%}" if i == 0 else "" + ) # Only show once + diff = metrics.mcp_optimizer_precision_at_5 - metrics.native_precision_at_5[approach] + summary_table.add_row( + "Precision@5" if i == 0 else "", + approach, + f"{metrics.native_precision_at_5[approach]:.2%}", + mcp_text, + f"{diff:.2%}", + ) + + # Precision@max + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_precision_at_max:.2%}" if i == 0 else "" + ) # Only show once + diff = ( + metrics.mcp_optimizer_precision_at_max - metrics.native_precision_at_max[approach] + ) + summary_table.add_row( + "Precision@max" if i == 0 else "", + approach, + f"{metrics.native_precision_at_max[approach]:.2%}", + mcp_text, + f"{diff:.2%}", + ) + + # Avg position + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_avg_position:.1f}" if i == 0 else "" + ) # Only show once + diff = metrics.mcp_optimizer_avg_position - metrics.native_avg_position[approach] + summary_table.add_row( + "Avg Position" if i == 0 else "", + approach, + f"{metrics.native_avg_position[approach]:.1f}", + mcp_text, + f"{diff:.1f}", + ) + + # Avg tools retrieved + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_avg_tools_retrieved:.1f}" if i == 0 else "" + ) # Only show once + diff = ( + metrics.mcp_optimizer_avg_tools_retrieved + - metrics.native_avg_tools_retrieved[approach] + ) + summary_table.add_row( + "Avg Tools Retrieved" if i == 0 else "", + approach, + f"{metrics.native_avg_tools_retrieved[approach]:.1f}", + mcp_text, + f"{diff:.1f}", + ) + + # Search time + for i, approach in enumerate(approaches): + mcp_text = ( + f"{metrics.mcp_optimizer_avg_search_time_s:.3f}" if i == 0 else "" + ) # Only show once + diff = ( + metrics.mcp_optimizer_avg_search_time_s - metrics.native_avg_search_time_s[approach] + ) + summary_table.add_row( + "Search Time (s)" if i == 0 else "", + approach, + f"{metrics.native_avg_search_time_s[approach]:.3f}", + mcp_text, + f"{diff:.3f}", + ) + + # Error rate + for i, approach in enumerate(approaches): + mcp_text = f"{metrics.mcp_optimizer_error_rate:.2%}" if i == 0 else "" # Only show once + summary_table.add_row( + "Error Rate" if i == 0 else "", + approach, + f"{metrics.native_error_rate[approach]:.2%}", + mcp_text, + f"{(metrics.mcp_optimizer_error_rate - metrics.native_error_rate[approach]):.2%}", + ) + + console.print(summary_table) + + # Token usage table + token_table = Table(title="Token Usage", show_header=True) + token_table.add_column("Approach", style="cyan") + token_table.add_column("Request Tokens", justify="right") + token_table.add_column("Response Tokens", justify="right") + token_table.add_column("Total Tokens", justify="right") + + for approach in approaches: + token_table.add_row( + f"Native ({approach})", + f"{metrics.native_avg_request_tokens[approach]:.0f}", + f"{metrics.native_avg_response_tokens[approach]:.0f}", + f"{metrics.native_avg_total_tokens[approach]:.0f}", + ) + token_table.add_row( + "MCP Optimizer", + f"{metrics.mcp_optimizer_avg_request_tokens:.0f}", + f"{metrics.mcp_optimizer_avg_response_tokens:.0f}", + f"{metrics.mcp_optimizer_avg_total_tokens:.0f}", + ) + + console.print(token_table) + + # Success counts + console.print("\n[bold]Success Counts:[/bold]") + for approach in approaches: + count = metrics.native_success_count[approach] + total = metrics.total_cases + console.print(f" Native ({approach}): {count}/{total}") + console.print( + f" MCP Optimizer: {metrics.mcp_optimizer_success_count}/{metrics.total_cases}" + ) + + # Precision@max counts (tool found at any position) + console.print("\n[bold]Tool Found Counts (Precision@max):[/bold]") + for approach in approaches: + # Count cases where precision@max = 1.0 + count = sum( + 1 + for r in report.individual_results + if r.native_precision_at_max.get(approach, 0.0) == 1.0 + ) + total = metrics.total_cases + console.print(f" Native ({approach}): {count}/{total}") + mcp_found_count = sum( + 1 for r in report.individual_results if r.mcp_optimizer_precision_at_max == 1.0 + ) + console.print(f" MCP Optimizer: {mcp_found_count}/{metrics.total_cases}") + console.print("\n") + + @staticmethod + def save_json_report(report: ComparisonReport, output_path: Path) -> None: + """Save full report to JSON file. + + Args: + report: Comparison report + output_path: Path to output JSON file + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w") as f: + json.dump(report.model_dump(), f, indent=2) + + logger.info("Saved JSON report", path=str(output_path)) + + @staticmethod + def save_csv_summary(report: ComparisonReport, output_path: Path) -> None: + """Save individual results to CSV for analysis. + + Args: + report: Comparison report + output_path: Path to output CSV file + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Get all approaches from first result + if not report.individual_results: + return + + approaches = list(report.individual_results[0].native_results.keys()) + + with open(output_path, "w", newline="") as f: + writer = csv.writer(f) + + # Header - dynamically create columns for each approach + header = ["query", "target_server", "target_tool"] + for approach in approaches: + header.extend( + [ + f"native_{approach}_selected_tool", + f"native_{approach}_is_correct", + f"native_{approach}_search_time_s", + f"native_{approach}_tokens", + f"native_{approach}_error", + ] + ) + header.extend( + [ + "mcp_selected_server", + "mcp_selected_tool", + "mcp_is_correct", + "mcp_precision_at_3", + "mcp_precision_at_5", + "mcp_tool_position", + "mcp_search_time_s", + "mcp_tokens", + "mcp_num_tools_returned", + "mcp_error", + ] + ) + writer.writerow(header) + + # Rows + for result in report.individual_results: + row = [ + result.test_case.query, + result.test_case.target_mcp_server_name, + result.test_case.target_tool_name, + ] + + # Add columns for each approach + for approach in approaches: + native_result = result.native_results[approach] + row.extend( + [ + native_result.selected_tool_name or "", + result.native_is_correct[approach], + f"{native_result.search_time_s:.3f}", + native_result.total_tokens, + native_result.error or "", + ] + ) + + # Add MCP columns + row.extend( + [ + result.mcp_optimizer_result.selected_server_name or "", + result.mcp_optimizer_result.selected_tool_name or "", + result.mcp_optimizer_is_correct, + f"{result.mcp_optimizer_precision_at_3:.2f}", + f"{result.mcp_optimizer_precision_at_5:.2f}", + result.mcp_optimizer_tool_position + if result.mcp_optimizer_tool_position is not None + else "", + f"{result.mcp_optimizer_result.search_time_s:.3f}", + result.mcp_optimizer_result.total_tokens, + result.mcp_optimizer_result.num_tools_returned, + result.mcp_optimizer_result.error or "", + ] + ) + + writer.writerow(row) + + logger.info("Saved CSV report", path=str(output_path)) + + @staticmethod + def save_accuracy_comparison_plot(report: ComparisonReport, output_path: Path) -> None: + """Save bar plot comparing Selection Accuracy vs Retrieval Accuracy. + + Args: + report: Comparison report + output_path: Path to output PNG file + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + metrics = report.aggregate_metrics + approaches = list(metrics.native_accuracy.keys()) + + # Prepare data - organize by metric type (Selection vs Retrieval) + approach_labels = [] + approach_colors = [] + + # Stacklok-inspired colors for dark theme + native_colors = ["#4A90E2", "#5BA3F5", "#6FB6FF"] # Blues for native approaches + mcp_color = "#FF6B35" # Orange accent for MCP Optimizer + + # Build approach labels and colors + for i, approach in enumerate(approaches): + approach_labels.append(f"Native\n({approach})") + approach_colors.append(native_colors[i % len(native_colors)]) + approach_labels.append("MCP\nOptimizer") + approach_colors.append(mcp_color) + + # Get accuracy values + selection_values = [metrics.native_accuracy[app] * 100 for app in approaches] + selection_values.append(metrics.mcp_optimizer_accuracy * 100) + + retrieval_values = [metrics.native_precision_at_max[app] * 100 for app in approaches] + retrieval_values.append(metrics.mcp_optimizer_precision_at_max * 100) + + # Create the plot with dark background + plt.style.use("dark_background") + fig, ax = plt.subplots(figsize=(12, 7)) + + # Set dark background colors + fig.patch.set_facecolor("#0A0E27") + ax.set_facecolor("#0A0E27") + + # Plot settings + num_approaches = len(approach_labels) + bar_width = 0.35 + group_gap = 1.5 # Gap between Selection and Retrieval groups + + # Create positions for bars + selection_positions = list(range(num_approaches)) + retrieval_positions = [x + num_approaches + group_gap for x in range(num_approaches)] + + # Plot Selection Accuracy bars + for pos, val, color in zip( + selection_positions, selection_values, approach_colors, strict=True + ): + ax.bar(pos, val, bar_width * 1.5, color=color, edgecolor="white", linewidth=0.5) + + # Plot Retrieval Accuracy bars + for pos, val, color in zip( + retrieval_positions, retrieval_values, approach_colors, strict=True + ): + ax.bar(pos, val, bar_width * 1.5, color=color, edgecolor="white", linewidth=0.5) + + # Add value labels on bars + def add_value_labels(positions, values): + for pos, val in zip(positions, values, strict=True): + ax.text( + pos, + val + 2, + f"{val:.1f}%", + ha="center", + va="bottom", + fontsize=10, + fontweight="bold", + color="white", + ) + + add_value_labels(selection_positions, selection_values) + add_value_labels(retrieval_positions, retrieval_values) + + # Set x-axis labels + all_positions = selection_positions + retrieval_positions + all_labels = approach_labels + approach_labels + ax.set_xticks(all_positions) + ax.set_xticklabels(all_labels, fontsize=10, color="white") + + # Add group labels + selection_center = (selection_positions[0] + selection_positions[-1]) / 2 + retrieval_center = (retrieval_positions[0] + retrieval_positions[-1]) / 2 + + ax.text( + selection_center, + -12, + "Selection Accuracy", + ha="center", + fontsize=13, + fontweight="bold", + color="#4A90E2", + ) + ax.text( + retrieval_center, + -12, + "Retrieval Accuracy", + ha="center", + fontsize=13, + fontweight="bold", + color="#4A90E2", + ) + + # Customize plot + ax.set_ylabel("Accuracy (%)", fontsize=13, fontweight="bold", color="white") + ax.set_title( + "Tool Search Performance: Selection vs Retrieval Accuracy", + fontsize=15, + fontweight="bold", + color="white", + pad=20, + ) + ax.set_ylim(0, 110) + ax.grid(axis="y", alpha=0.2, linestyle="--", color="white") + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.spines["left"].set_color("white") + ax.spines["bottom"].set_color("white") + ax.tick_params(colors="white") + + plt.tight_layout() + plt.savefig(output_path, dpi=300, bbox_inches="tight", facecolor="#0A0E27") + plt.close() + + # Reset style to default + plt.style.use("default") + + logger.info("Saved accuracy comparison plot", path=str(output_path)) diff --git a/examples/anthropic_comparison/tool_converter.py b/examples/anthropic_comparison/tool_converter.py new file mode 100644 index 0000000..6e80267 --- /dev/null +++ b/examples/anthropic_comparison/tool_converter.py @@ -0,0 +1,167 @@ +"""Tool conversion utilities for converting MCP tools to Anthropic format.""" + +import re + +from pydantic import BaseModel, field_validator + + +class ToolParameter(BaseModel): + """Represents a tool parameter with validation.""" + + name: str + type: str + description: str + is_optional: bool = False + items: dict | None = None # For array types + + @field_validator("name") + @classmethod + def sanitize_name(cls, v: str) -> str: + """Sanitize parameter name to match ^[a-zA-Z0-9_.-]{1,64}$""" + sanitized = re.sub(r"[^a-zA-Z0-9_.-]", "_", v) + sanitized = re.sub(r"_+", "_", sanitized).strip("_")[:64] + if not sanitized: + sanitized = "param" + return sanitized + + +class AnthropicTool(BaseModel): + """Represents an Anthropic API tool with validation.""" + + name: str + description: str + input_schema: dict + defer_loading: bool | None = True + + @field_validator("name") + @classmethod + def sanitize_name(cls, v: str) -> str: + """Sanitize tool name to match ^[a-zA-Z0-9_-]{1,128}$""" + sanitized = re.sub(r"[^a-zA-Z0-9_-]", "-", v) + sanitized = re.sub(r"-+", "-", sanitized).strip("-")[:128] + if not sanitized: + sanitized = "tool" + return sanitized + + +class ToolConverter: + """Converts MCP tools to Anthropic format with validation.""" + + def __init__(self): + self.tool_name_counts: dict[str, int] = {} + + def parse_parameter_type(self, param_desc: str) -> tuple[str, str, bool]: + """ + Parse parameter description to extract type, description, and optionality. + Returns: (type, description, is_optional) + """ + match = re.match(r"\(([^)]+)\)\s*(.*)", param_desc) + if not match: + return "string", param_desc, False + + type_info = match.group(1).lower() + description = match.group(2).strip() + + is_optional = ( + "optional" in type_info + or "opt" in type_info + or type_info.startswith("optional,") + or type_info.endswith(", optional") + ) + + # Determine JSON Schema type (check arrays first before scalar types) + if "array" in type_info or "[]" in type_info or "list" in type_info: + if "string" in type_info: + return "array_string", description, is_optional + elif "object" in type_info: + return "array_object", description, is_optional + elif "number" in type_info or "int" in type_info: + return "array_number", description, is_optional + else: + return "array", description, is_optional + elif "object" in type_info or "dict" in type_info: + return "object", description, is_optional + elif "string" in type_info or "str" in type_info: + return "string", description, is_optional + elif "number" in type_info or "int" in type_info or "float" in type_info: + return "number", description, is_optional + elif "bool" in type_info: + return "boolean", description, is_optional + else: + return "string", description, is_optional + + def convert_parameter(self, param_name: str, param_desc: str) -> tuple[ToolParameter, dict]: + """Convert a single parameter to ToolParameter and its schema.""" + param_type, description, is_optional = self.parse_parameter_type(param_desc) + + # Create parameter + items = None + json_type = param_type + + if param_type == "array_string": + json_type = "array" + items = {"type": "string"} + elif param_type == "array_object": + json_type = "array" + items = {"type": "object"} + elif param_type == "array_number": + json_type = "array" + items = {"type": "number"} + elif param_type == "array": + json_type = "array" + items = {"type": "string"} # Default to string array + + param = ToolParameter( + name=param_name, + type=json_type, + description=description, + is_optional=is_optional, + items=items, + ) + + # Build schema dict + schema = {"type": json_type, "description": description} + if items: + schema["items"] = items + + return param, schema + + def make_unique_name(self, base_name: str) -> str: + """Make tool name unique by appending a counter if needed.""" + if base_name not in self.tool_name_counts: + self.tool_name_counts[base_name] = 1 + return base_name + + self.tool_name_counts[base_name] += 1 + return f"{base_name}-{self.tool_name_counts[base_name]}" + + def convert_tool(self, server: dict, tool: dict) -> AnthropicTool: + """Convert an MCP tool to Anthropic format.""" + properties = {} + required = [] + + if "parameter" in tool and tool["parameter"]: + for param_name, param_desc in tool["parameter"].items(): + param, schema = self.convert_parameter(param_name, param_desc) + properties[param.name] = schema + + if not param.is_optional: + required.append(param.name) + + # Create base tool name + server_prefix = server["name"].lower() + tool_name = tool["name"] + base_name = f"{server_prefix}-{tool_name}" + + # Make name unique + unique_name = self.make_unique_name(base_name) + + return AnthropicTool( + name=unique_name, + description=tool["description"], + input_schema={ + "type": "object", + "properties": properties, + "required": required, + }, + ) diff --git a/examples/anthropic_comparison/tool_search_comparison.py b/examples/anthropic_comparison/tool_search_comparison.py new file mode 100644 index 0000000..c2b0734 --- /dev/null +++ b/examples/anthropic_comparison/tool_search_comparison.py @@ -0,0 +1,126 @@ +import asyncio +import os +from pathlib import Path + +import click +from comparison_orchestrator import ComparisonOrchestrator +from results_exporter import ResultsExporter + +from mcp_optimizer.configure_logging import configure_logging + + +@click.command() +@click.option( + "--dataset", + type=click.Path(exists=True, path_type=Path), + default=Path(__file__).parent / Path("mcp_tools_cleaned_tests_claude-sonnet-4.json"), + help="Path to test dataset JSON", +) +@click.option( + "--output-file", + type=click.Path(resolve_path=True, dir_okay=False, file_okay=True, path_type=Path), + default=Path(__file__).parent / Path("results.json"), + help="Output file path for results JSON", +) +@click.option( + "--limit", + type=int, + default=None, + help="Limit number of test cases (for testing)", +) +@click.option( + "--max-concurrency", + type=int, + default=10, + help="Max concurrent test executions", +) +@click.option( + "--llm-model", + type=str, + default="anthropic/claude-sonnet-4.5", + help="LLM model for MCP Optimizer agent", +) +@click.option( + "--resume", + is_flag=True, + help="Resume from existing results file (skips completed tests, retries errors)", +) +@click.option( + "--dry-run", + is_flag=True, + help="Validate setup without running comparison", +) +def main( + dataset: Path, + output_file: Path, + limit: int | None, + max_concurrency: int, + llm_model: str, + resume: bool, + dry_run: bool, +): + """Run MCP Optimizer vs Anthropic Native comparison.""" + + # Setup logging + configure_logging("INFO", rich_tracebacks=False, colored_logs=True) + + # Check for API keys + anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY") + openrouter_api_key = os.environ.get("OPENROUTER_API_KEY") + + if not anthropic_api_key or not openrouter_api_key: + raise ValueError("ANTHROPIC_API_KEY and OPENROUTER_API_KEY required") + + # Create output directory if it doesn't exist + output_file.parent.mkdir(parents=True, exist_ok=True) + + # Determine resume path + resume_from = output_file if resume and output_file.exists() else None + + if resume and resume_from: + print(f"Resuming from existing results: {output_file}") + elif resume and not resume_from: + print(f"Resume flag set but no existing results found at {output_file}, starting fresh") + + # Create orchestrator + orchestrator = ComparisonOrchestrator( + dataset_path=dataset, + anthropic_api_key=anthropic_api_key, + openrouter_api_key=openrouter_api_key, + limit=limit, + max_concurrency=max_concurrency, + llm_model=llm_model, + resume_from=resume_from, + output_file=output_file, + ) + + # Run dry-run validation if requested + if dry_run: + try: + orchestrator.dry_run() + print("\n✓ Dry-run validation passed - setup is ready") + return + except ValueError as e: + print(f"\n✗ Dry-run validation failed: {e}") + raise SystemExit(1) from e + + # Run comparison + report = asyncio.run(orchestrator.run()) + + # Export results + ResultsExporter.print_console_summary(report) + + # Save to JSON only + ResultsExporter.save_json_report(report, output_file) + + # Generate and save accuracy comparison plot + plot_file = output_file.parent / f"{output_file.stem}_accuracy_comparison.png" + ResultsExporter.save_accuracy_comparison_plot(report, plot_file) + + print(f"\n✓ Results saved to {output_file}") + print(f"✓ Accuracy comparison plot saved to {plot_file}") + + +if __name__ == "__main__": + # CLI interface using click + main() diff --git a/pyproject.toml b/pyproject.toml index c44c315..c68ffaf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,11 @@ security = [ "pip-audit>=2.10.0", "cyclonedx-bom>=7.2.1", ] 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