AI Loop Library MCP server

A read-only MCP server that gives coding agents the AI Loop Library: 63+ bounded, verifiable work loops with a trigger, one-change-per-round discipline, a verification check, durable state, a stop condition, a budget, and human approval gates.

The design premise: the calling agent is the best ranker available — it knows the operator's repo, data, and constraints, and this server doesn't. So the tools hand the agent clean, compact evidence instead of pretending to judge for it: browse_catalog returns the whole library as a ~2k-token digest to judge from, pick_loop_for_goal returns an honest lexically-ranked shortlist with a confidence signal (never a single blind verdict), and render_run_protocol turns the chosen loop into an executable markdown protocol with a state-file skeleton, stop conditions, and a paste-ready prompt. critique_loop lints any loop design against the anti-pattern rubric, and design_loop scaffolds a new spec when nothing in the catalog fits.

Single file, Python 3.9+ standard library only. No dependencies, no auth, no write tools.

Install

From a clone of this repo:

python3 server.py --self-test   # verify: 43 offline checks
python3 server.py --eval        # 20 golden ranking queries vs the live catalog

Or grab the single file straight from the live site:

mkdir -p ~/.ai-loop-library
curl -fsSL https://ailooplibrary.com/mcp/server.py -o ~/.ai-loop-library/server.py
python3 ~/.ai-loop-library/server.py --self-test

Claude Code

claude mcp add ai-loop-library -- python3 ~/.ai-loop-library/server.py

Cursor / generic MCP client

{
  "mcpServers": {
    "ai-loop-library": {
      "command": "python3",
      "args": ["/absolute/path/to/server.py"],
      "env": {
        "AI_LOOP_LIBRARY_CATALOG_URL": "https://ailooplibrary.com/catalog.json"
      }
    }
  }
}

Optional: pip install

pip install -e .             # installs the ai-loop-library-mcp console script
claude mcp add ai-loop-library -- ai-loop-library-mcp

Catalog source

Resolution order:

1. AI_LOOP_LIBRARY_CATALOG_PATH — local JSON file (catalog.json or data/loops.json shape)
2. AI_LOOP_LIBRARY_CATALOG_URL — defaults to https://ailooplibrary.com/catalog.json
3. Repo-local fallback (../catalog.json, ../data/loops.json) when the server runs inside the site repo; otherwise an embedded 2-loop sample keeps --self-test fully offline

Fetched catalogs are cached in memory for 5 minutes.

Tools

Tool	What it does
browse_catalog(category?)	The whole catalog as a ~2k-token digest (id, category, use_when, verifier strength) — one call, then the agent judges against operator context
search_loops(query, category?, limit?)	Ranked loops with a one-line why-matched
get_loop(id_or_slug)	Full loop spec + canonical URL, with verifier strength and loop kind
pick_loop_for_goal(goal, constraints?, limit?)	Lexically ranked shortlist (5 by default) with use_when, verification, and an honest confidence signal — the agent makes the final call
render_run_protocol(id_or_slug, goal?, risk_posture?, kind?, max_rounds?, max_minutes?)	Executable markdown protocol: done contract, one-change-per-round, verification, state files, stop conditions, budget, risk-colored approval boundary, proof format. Scheduled-tick business loops (SEO, ads, product metrics) get experiment logs, undo-losers discipline, and notify-the-human ticks
critique_loop(loop_description)	Deterministic lint against the anti-pattern rubric (verifier, stop condition, budget, one-change-per-round, state, MVL, risk gates…) — 0–10 score with per-check fixes
design_loop(goal, constraints?, cadence?, context?)	Scaffold a new loop spec from a stated bottleneck, with a domain-matched verifier suggestion and the nearest catalog loops
list_categories()	Category counts with library filter URLs
catalog_stats()	Loop count, featured loops, last_updated, catalog source

All tools declare readOnlyHint. Resources: ailooplibrary://catalog and ailooplibrary://loop/{id}.

Ranking is a transparent lexical heuristic — IDF-weighted keyword overlap (computed from the catalog at load, so template boilerplate scores near zero) with light stemming, a small documented synonym/expansion map, damped brand tokens, and a goal-term-to-category map. It is documented in server.py (_score_loop, SYNONYMS_RAW, CATEGORY_HINTS) and labeled as such in tool output. --eval holds it to 20 golden queries at a ≥85% top-3 hit rate. No model, no magic — and when confidence is low, the output says so.

Design constraints

• Read-only. No write tools, no shell execution of user code, no posting, no auth, no PII.
• stdio transport only (newline-delimited JSON-RPC 2.0, MCP protocol 2024-11-05 through 2025-06-18).
• Errors from tools return isError: true with a plain-text explanation, never a crash.

Related

• ailooplibrary.com — the library itself: loop specs, stop conditions, templates, and research
• ailooplibrary.com/for-agents/ — install page, including the Claude Code skill and a zero-install path
• Claude Code skill — works without MCP by fetching catalog.json

License

MIT