Humanswith.ai MCP Token Savers

Repo renamed 2026-05-24: g-shevchenko/hwai-mcp-stack → g-shevchenko/mcp-token-savers. Old URLs still redirect via GitHub. The local install path ~/.hwai/hwai-mcp-stack/ is preserved for backward compatibility — existing users do not need to reinstall.

Local-first Token Efficiency Platform for Agentic IDEs.

The product goal is to help Claude Code, Codex, Cursor, and Windsurf spend fewer tokens on noisy context while preserving the quality bar. The technical core is HWAI Context Router: a local pre-reasoning layer that routes tasks to the smallest useful prep/evidence MCP before a frontier model spends tokens.

The public stack is local-only. It does not install external web/search/crawl MCPs by default, and it does not require API keys for the default or full profile.

Deep-dive write-up: How I cut my Claude Code token usage by 75.5% with 17 local MCPs — public research article with the pattern, the measured numbers, and the comparison against Cursor, Cody, Continue, Aider, Firecrawl, LLMLingua, Martian, RouteLLM, Helicone, Langfuse, and the Anthropic / Cloudflare Code Execution with MCP primary sources. Read it on gregshevchenko.com.

Why

Modern coding agents are strongest when they see the right evidence, not the largest possible prompt. HWAI Context Router gives them deterministic prep tools:

• find the right files before editing;
• compact huge logs, specs, traces, screenshots, and dependency reports;
• catch repo/documentation drift early;
• keep raw local evidence local by default;
• work across Claude Code, Codex, Cursor, and Windsurf with the same MCP config.

In product terms, the 17 local MCP servers are modules behind one Token Efficiency Platform. Agents should experience one workflow: classify the task, prepare the right compact local evidence, and reserve frontier model context for judgment rather than raw search, logs, traces, or screenshots.

Verify First, Then Install

Recommended inspect-first path:

git clone https://github.com/g-shevchenko/mcp-token-savers.git
cd mcp-token-savers
bash scripts/agent-preinstall-check.sh
bash install.sh --dry-run
bash install.sh

This gives a human or agentic IDE a concrete trust path before anything is installed: inspect the installer, run the public audit, run doctor, and see the exact write targets in dry-run output.

See Trust and verification and Verify before install. Agents can also read the machine-readable trust manifest.

60-Second Install

Fast local-first install after inspection:

/bin/bash -lc "$(curl -fsSL https://raw.githubusercontent.com/g-shevchenko/mcp-token-savers/main/install.sh)"

For repeatable installs, prefer a release tag or commit SHA instead of main:

HWAI_MCP_BRANCH=76540dcfbcd12284fc2b783d22c5c091624eaf82 \
/bin/bash -lc "$(curl -fsSL https://raw.githubusercontent.com/g-shevchenko/mcp-token-savers/76540dcfbcd12284fc2b783d22c5c091624eaf82/install.sh)"

Install all 17 local MCP servers:

HWAI_MCP_PROFILE=full /bin/bash -lc "$(curl -fsSL https://raw.githubusercontent.com/g-shevchenko/mcp-token-savers/main/install.sh)"

Install only for selected clients:

HWAI_MCP_PROFILE=full HWAI_MCP_CLIENTS=codex,cursor /bin/bash -lc "$(curl -fsSL https://raw.githubusercontent.com/g-shevchenko/mcp-token-savers/main/install.sh)"

Requirements: macOS/Linux shell, git, Node.js, and npm.

After installation, restart Claude Code, Codex, Cursor, or Windsurf, or open a new chat so stdio MCP configs reload.

By default the installer also teaches the local project how to use the stack: Claude Code, Codex, Cursor, and Windsurf get repo-local guidance, managed Humanswith.ai blocks/rules, and a natural-language trigger vocabulary. This is intentional for beginner-friendly Agentic Engineering workflows: users should be able to write "find where this is implemented", "summarize this CI log", or "check this screenshot", and the agent should know which MCP to consider.

The installer updates only managed Humanswith.ai blocks/rules and creates docs/humanswithai-mcp-stack.md. To skip that project-teaching layer for a config-only repair, set HWAI_MCP_AGENT_DOCS=skip.

What You Get

Need	MCPs
Route ambiguous agent tasks to the right prep tool	HWAI Context Router via router-lite-mcp
Retrieve compact repo context before edits	retrieval-mcp, context-prep-mcp
Understand code structure and history	language-graph-mcp, repo-history-mcp
Run local static checks and quality gates	static-analysis-mcp, repo-quality-gate-mcp
Keep a growing repo clean	repo-hygiene-mcp, docs-hygiene-mcp, docs-sync-mcp
Review contracts and dependency risk	contract-schema-mcp, dependency-risk-mcp
Build regression datasets from real misses	golden-dataset-mcp, agent-trace-mcp
Debug browser traces and visual changes	playwright-trace-mcp, vision-mcp, visual-baseline-mcp
Track test/feature pass-fail contracts between AI agent roles	test-results-mcp
Enforce TDD discipline at MCP-tool level (edit gate + test immutability + verify red)	tdd-gate-mcp
Property-based testing with hypothesis / fast-check (counterexample extraction)	pbt-runner-mcp

Measurement-Driven Routing

Different deterministic compressors win in different regimes. The same input, the same agent task, the same output budget — but the right compressor depends on whether the LLM needs a one-line gist, a specific fact buried in document metadata, or a full extractive summary. Picking the wrong tier silently loses quality.

Humanswith.ai operates an internal measurement-driven routing layer that maps (input_size, output_budget, task_type) to the Pareto-best compressor for that case. The mapping is deterministic — no LLM in the routing path — and the rules are derived from a paired-corpus benchmark that scores each candidate compressor on both byte-saving and content-preservation. Inputs below a length threshold are routed past compression entirely (some compressors inflate short inputs by adding wrapper sections).

The philosophy: measurement is the moat, not the compressor.

• Tools like mcp-sophon (below) are valuable in their regime.
• The agency value is knowing when to call which one.
• That routing intelligence is HWAI's internal stack; this public repo exposes the measured vendor candidates, not the routing engine.

A long-form write-up of the methodology and per-tier measurements lives at the research article.

Two Primary Axes: Byte Saving AND Cache-Friendliness

Byte saving is necessary but not sufficient. The second metric that decides production cost is whether the compressor's output is byte-identical across runs of the same input. Byte-identical output lets the downstream provider's prefix cache reuse work from prior turns — turning a measured byte saving into a real cost reduction. Non-deterministic output defeats the cache: every turn looks like a fresh prompt to the provider and pays the full prefill again, often eating the byte saving outright.

This framing is canon in the agents-best-practices reference (MIT, provider-neutral synthesis of OpenAI / Anthropic / MCP guidance). The core rule: stable prefix, dynamic suffix. Tool definitions and static instructions appear first in deterministic order; dynamic runtime state appears at the end. Any volatile value injected before a stable block destroys the cache for every downstream turn that shares the prefix.

Our deterministic-compressor benchmark records both axes:

• Byte-saving ratio = output_chars / input_chars (lower is better).
• Cache-friendly score = fraction of fixtures whose output is byte-identical across N ≥ 2 runs of the same input (unique_md5_count == 1). 100% means full byte-stability; lower means downstream cache reuse drops proportionally.

A compressor that wins byte savings but loses output stability is a single-axis benchmark hiding the other half of the cost. We report both axes for every measured MCP — including failure modes the vendor docs don't disclose. See the research article's cache-friendliness section for the long-form treatment.

Measured Vendor MCPs

The repo's local MCPs are the core stack. We also measure third-party MCPs through our deterministic-compressor benchmark (input → output ratio + CV variance + LLM-judged content preservation) and publish the honest result — including failure modes the vendor docs don't disclose.

mcp-sophon (npm, MIT, by lacausecrypto) — measured 2026-05-24

Byte saving (passes the bar): 92.7% mean char-saving on a 15-fixture realistic corpus × N=5 (75 measurements). Latency 60–200 ms per call.

Cache-friendly score: 100% (15/15 fixtures byte-stable). CV = 0.0 across all runs — sophon's section-selector is deterministic by construction (no LLM in the path), so downstream provider prefix-cache reuse works correctly across turns. This is the necessary condition for byte savings to translate into real production cost reduction.

Content preservation (caveat-heavy): on the same 15 fixtures with one gpt-4o-mini-judged QA pair per fixture, sophon at --max-tokens 500 preserved 10/15 = 67% of correct answers vs full-context baseline (87%). Pattern: sophon's section-selector drops document headers, titles, bylines, status codes, and version metadata in favor of body content.

--query parameter is ignored in CLI mode at default budget — we tested 4 different queries on the same input and got byte-identical output (same SHA-256). Sophon's "query-aware" branding does not hold at this configuration.

When to route to sophon

✅ USE for: long-document summarization where the answer is in body content (≥1000 chars input). E.g. "give me the gist of this 24k-char build log."

❌ DO NOT USE for: specific-fact lookups (version numbers, IDs, status codes, author bylines, config values). Sophon may drop the answer entirely.

⚠️ Caller plumbing required: length-gate ≥ 1000 chars (sophon inflates shorter inputs by adding a <general>...</general> wrapper).

Install (separate from this stack)

npm install -g mcp-sophon   # MIT, no API keys
sophon doctor                # verify

Sophon is not bundled with this stack — it's a third-party MCP we've measured. Install it on your own; route to it per the guidance above.

Methodology

Full measurement details: research article on the token-economy axes (C1/C2/C3). Our deterministic-compressor harness measures any MCP that exposes a pure text → text interface and emits per-fixture + corpus-level verdicts against documented CV/ratio/quality bars.

Profiles

Profile	Installs	Best for
core	6 MCPs	First install, safe local repo work
repo	17 MCPs	Large codebases, docs, hygiene, local regression cases
browser-debug	10 MCPs	Playwright traces, screenshots, visual checks
full	20 MCPs	All local token-efficiency MCPs, no external context required

Verification

The installer runs doctor automatically. You can rerun it anytime:

~/.hwai/hwai-mcp-stack/mcp/bin/hwai-mcp.mjs doctor \
  --manifest=~/.hwai/hwai-mcp-stack/mcp/manifest.json \
  --source-root=~/.hwai/hwai-mcp-stack/mcp/source \
  --profile=full

Expected result for the full profile:

services: 17
ok: 17
needs_attention: 0
warnings: 0

Token Efficiency Claims

This stack is designed to reduce token waste by preparing compact local evidence before an agent spends frontier-model context. It is fair to claim:

• it can reduce prompt stuffing for repo-wide questions by retrieving likely files/snippets first;
• it can reduce noisy context from long logs, specs, traces, and screenshots by turning them into compact evidence;
• it can reduce repeated manual search by teaching Claude Code, Codex, Cursor, and Windsurf the same local trigger vocabulary;
• raw local evidence stays local by default.

Measured local dogfood evidence (details):

• In a 2026-04-30 local deterministic dogfood eval on 12 reviewed-public tasks, Humanswith.ai MCP Stack reduced aggregate context-token usage by 75.5% and aggregate total-token usage by 70.5% versus the baseline path.
• The same run passed the quality gate: baseline success was 91.7%, stack success was 100.0%, and critical false positives did not increase.
• Context-token reduction by family ranged from 35.0% to 80.8% across repo hygiene, traces, screenshots, logs, retrieval, and compression tasks.

This is internal dogfood evidence, not an external benchmark or leaderboard claim.

Do not claim a universal percentage reduction from the public README. Token savings depend on repo size, task type, agent behavior, and whether the agent would otherwise paste entire files/logs/screenshots into context.

Publicly verified scope:

• core and full profile install dry-runs;
• doctor checks for bundled local services;
• local repo retrieval and context-prep smoke paths;
• local screenshot/trace preparation surfaces in the browser-debug profile;
• public release audit that checks for secrets and internal references.

See Token efficiency claims for the exact wording to use in public materials.

Benchmark Your Own Stack

The benchmark/ directory contains the same primitives we use to measure compressors under both axes (byte saving AND cache-friendliness), stripped of any HWAI-specific fixtures or measurements. Pure Python, stdlib only, no network.

# Run the harness on the example fixtures (5 neutral samples):
cd benchmark
python3 run_bench.py --compressor first200 --fixtures examples/fixtures.jsonl --repeat 5

# Audit any MCP / source tree against the 12 DSA cache-killing anti-patterns:
python3 anti_pattern_audit.py --mcp path/to/your-mcp/src

# Run the unit tests:
python3 -m pytest tests/ -q

Plug your own compressor as a (str) -> str function. See benchmark/README.md for the full API + how to register your own compressor in the harness. The framing for the cache-friendliness axis comes from the canonical DSA reference at github.com/DenisSergeevitch/agents-best-practices (MIT, provider-neutral).

Privacy And Security

• Do not trust the repository from GitHub stars or social proof; inspect and verify it with bash scripts/agent-preinstall-check.sh.
• Local repo/file evidence stays local by default.
• Durable traces live under ~/.hwai/<service-name>.
• Generated client configs point to local stdio wrappers.
• The default install path does not use sudo and does not install daemons.
• Aggregate reports should not export raw code, prompts, URLs, screenshots, lockfiles, env files, or private docs.
• The public full profile is local-only and does not require API keys.
• Never commit ~/.hwai/mcp-stack/env or generated request logs.

Repository Layout

.
├── install.sh                 # one-command public installer
├── mcp/
│   ├── manifest.json          # profiles and server catalog
│   ├── install.sh             # local bundle installer
│   ├── bin/hwai-mcp.mjs       # install/doctor CLI
│   ├── docs/                  # module docs
│   └── source/services/       # bundled local MCP servers
└── PUBLIC_RELEASE_AUDIT.md    # release safety checklist

Documentation

External long-form context and background:

• Research article — How I cut my Claude Code token usage by 75.5% with 17 local MCPs
• Author profile — Gregory Shevchenko (founder of Humanswith.ai)

Repo-internal:

• MCP bundle details

• Module docs

• Agent autopilot docs

• Token efficiency claims

• Local dogfood eval - 2026-04-30

• Trust and verification

• Verify before install

• Machine-readable trust manifest

• Public release audit

• Security policy

Daily Readiness Collector (Local launchd)

The optional daily measurement-readiness aggregator runs from a launchd LaunchAgent. macOS TCC blocks launchd-spawned processes from reading ~/Documents, so running the aggregator directly from a clone under ~/Documents fails with Operation not permitted and silently stops producing reports.

Install a standalone, TCC-safe copy into a non-protected directory instead:

# from an interactive shell (it has Documents access)
bash scripts/install-token-efficiency-collector.sh
# verify the install is byte-identical to the repo source closure
bash scripts/install-token-efficiency-collector.sh --check

This copies the builtins-only aggregator closure (no extra runtime deps) into ~/.hwai/token-efficiency-collector/, preserving the relative scripts/ + mcp/source/scripts/ + mcp/manifest.json layout the scripts expect. Point the LaunchAgent's ProgramArguments at ~/.hwai/token-efficiency-collector/scripts/greg-dogfood-catchup.sh and add an EnvironmentVariables PATH (launchd's minimal PATH does not include /usr/local/bin, so node would otherwise not be found). Re-run --check after pulling updates to detect a stale install.

A custom MCP scope manifest can be passed with --manifest=/abs/path/to/manifest.json (default: this repo's mcp/manifest.json).

Roadmap

• Improve local context compression for logs, specs, traces, and screenshots.
• Add more one-click client installers.
• Publish public-safe task examples for repo retrieval, log prep, trace prep, and screenshot prep.
• Keep the public install local-first and API-key-free by default.

License

MIT. See LICENSE.