Add remote repository support and AI-powered release notes generation

README.md

@@ -60,10 +60,81 @@ utility-mcp-server/
 ### Current MCP Tools
 
 1. **`generate_release_notes`**: Generates structured release notes from git commits and tags
+   - **Remote Repository Support**: Fetch commits from GitHub/GitLab via API (no local clone needed)
+   - **Local Repository Support**: Fallback to local git repositories via subprocess
+   - **AI Agent Integration**: Returns structured data for intelligent AI-driven categorization
+   - **Provider Pattern**: Extensible architecture for adding new git hosting services
 
 ### Release Notes Tool
 
-The `generate_release_notes` tool automatically creates comprehensive release notes from git commits, categorizing changes into features, enhancements, bug fixes, and breaking changes. See [USE_CASES.md](USE_CASES.md) for detailed use cases and integration scenarios.
+The `generate_release_notes` tool automatically creates comprehensive release notes from git commits. It supports both remote repositories (GitHub, GitLab) and local repositories, with optional AI agent integration for intelligent categorization.
+
+#### Remote Repository Support
+
+The tool can fetch commits directly from GitHub and GitLab repositories without requiring a local clone:
+
+**GitHub:**
+- Provide `repo_url` like `https://github.com/owner/repo`
+- Optionally provide `github_token` (or set `GITHUB_TOKEN` environment variable)
+- Uses PyGithub API to fetch commits and PR associations
+
+**GitLab:**
+- Provide `repo_url` like `https://gitlab.com/owner/repo`
+- Optionally provide `gitlab_token` (or set `GITLAB_TOKEN` environment variable)
+- Uses python-gitlab API to fetch commits and MR associations
+
+**Local Repository (fallback):**
+- Provide `repo_path` to a local git repository
+- Uses git subprocess commands
+
+#### AI Agent Integration
+
+The tool returns structured commit data **with embedded AI instructions** for intelligent categorization:
+- Returns raw commit list with hashes, messages, authors, dates, and PR/MR numbers
+- **Includes `ai_instructions`** field with comprehensive guidance on categorization
+- Instructions travel with data - ensures consistent categorization across all AI agents
+- AI creates dynamic categories based on actual changes instead of predefined patterns
+- Better context understanding than regex-based categorization
+
+**Example response structure:**
+```python
+result = await generate_release_notes(
+    version="v1.0.0",
+    previous_version="v0.9.0",
+    repo_url="https://github.com/owner/repo"
+)
+
+# Returns:
+{
+    "status": "success",
+    "data": {
+        "commits": [...],
+        "version": "v1.0.0",
+        ...
+    },
+    "ai_instructions": {
+        "role": "release_notes_categorizer",
+        "task": "Analyze commits and create intelligent release notes",
+        "guidelines": [
+            "Create dynamic categories based on actual changes",
+            "Group related commits intelligently",
+            "Understand context beyond pattern matching",
+            ...
+        ],
+        "categorization_strategy": {...},
+        "suggested_sections": {...},
+        "output_format": {...}
+    }
+}
+```
+
+**Why instructions are embedded:**
+- ✅ Instructions version-controlled with tool
+- ✅ Consistent categorization across all workflows
+- ✅ Self-documenting - AI knows how to use the data
+- ✅ No need to duplicate instructions in each workflow
+
+See [USE_CASES.md](USE_CASES.md) for detailed use cases and integration scenarios.
 
 ## Installation
 
@@ -200,6 +271,51 @@ Once configured, you can use the `generate_release_notes` tool in your conversat
 
 The tool will analyze git commits between the specified tags and generate structured release notes with categorized changes.
 
+## Usage Examples
+
+### Remote GitHub Repository
+
+```python
+# Using with AI agent integration
+result = await generate_release_notes(
+    version="v1.0.0",
+    previous_version="v0.9.0",
+    repo_url="https://github.com/owner/repo",
+    github_token=os.getenv('GITHUB_TOKEN'),  # Optional, but recommended
+    return_raw_data=True  # Returns structured data for AI processing
+)
+
+# Standard formatted output
+result = await generate_release_notes(
+    version="v1.0.0",
+    previous_version="v0.9.0",
+    repo_url="https://github.com/owner/repo",
+    github_token=os.getenv('GITHUB_TOKEN')
+)
+```
+
+### Remote GitLab Repository
+
+```python
+result = await generate_release_notes(
+    version="v2.0.0",
+    previous_version="v1.5.0",
+    repo_url="https://gitlab.com/owner/repo",
+    gitlab_token=os.getenv('GITLAB_TOKEN')  # Optional for public repos
+)
+```
+
+### Local Repository
+
+```python
+# Existing behavior - still fully supported
+result = await generate_release_notes(
+    version="v1.0.0",
+    previous_version="v0.9.0",
+    repo_path="/path/to/local/repo"
+)
+```
+
 ## Running as HTTP Server
 
 For development, testing, or integration with other tools, you can run the MCP server as an HTTP service.

pyproject.toml

@@ -7,14 +7,14 @@ packages = ["utility_mcp_server"]
 
 [project]
 name = "utility-mcp-server"
-version = "0.1.1"
-description = "A utility Model Context Protocol (MCP) server"
+version = "0.2.0"
+description = "A utility Model Context Protocol (MCP) server with remote repository support"
 readme = "README.md"
 license = {text = "Apache-2.0"}
 authors = [
     {name = "Jitendra Yejare", email = "jyejare@redhat.com"}
 ]
-keywords = ["mcp", "model-context-protocol", "release-notes", "fastmcp", "ai-tools"]
+keywords = ["mcp", "model-context-protocol", "release-notes", "fastmcp", "ai-tools", "github", "gitlab"]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
@@ -39,6 +39,9 @@ dependencies = [
     "psycopg==3.2.3",
     "itsdangerous==2.2.0",
     "requests-oauthlib==2.0.0",
+    "PyGithub>=2.1.1",
+    "python-gitlab>=4.0.0",
+    "packaging>=23.0",
 ]
 
 [project.optional-dependencies]