The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Ailooplibrary MCP listing page.
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.
From a clone of this repo:
Or grab the single file straight from the live site:
Resolution order:
AI_LOOP_LIBRARY_CATALOG_PATH — local JSON file (catalog.json or data/loops.json shape)AI_LOOP_LIBRARY_CATALOG_URL — defaults to https://ailooplibrary.com/catalog.json../catalog.json, ../data/loops.json) when the server runs inside
the site repo; otherwise an embedded 2-loop sample keeps --self-test fully offlineFetched catalogs are cached in memory for 5 minutes.
| 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.
isError: true with a plain-text explanation, never a crash.