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Ailooplibrary MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/21/2026, 5:31:57 AM

Ailooplibrary MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

Read-only access to the AI Loop Library catalog: 63+ bounded, verifiable agent work loops. pickloopforgoal recommends a loop for a stated goal and renderrunprotocol emits an executable markdown protocol with verification, stop conditions, budgets, and approval gates. Single-file, stdlib-only stdio server.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "paultaki-ailooplibrary-mcp": {
      "url": "https://ailooplibrary.com/mcp/server.py"
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

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 β€” 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:

bash
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:

bash
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

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

Cursor / generic MCP client

config.json
{
  "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

Terminal
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 β€” local JSON file (catalog.json or data/loops.json shape)
  2. AI_LOOP_LIBRARY_CATALOG_URL β€” 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

ToolWhat it does
browse_catalog(category?)The whole catalog as a ~2k-token digest (id, category, use_when, verifier strength) β€” 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 β€” 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…) β€” 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 β€” 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 β‰₯85% top-3 hit rate. No model, no magic β€” 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 β€” the library itself: loop specs, stop conditions, templates, and research
  • ailooplibrary.com/for-agents/ β€” install page, including the Claude Code skill and a zero-install path
  • Claude Code skill β€” works without MCP by fetching catalog.json

License

MIT

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

Last commit
1mo ago
Most recent push to the default branch.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Ailooplibrary MCP

paultaki/ailooplibrary-mcp is a hosted MCP server. Add it as a remote server in your client's config: "mcpServers": { "ailooplibrary-mcp": { "url": "https://ailooplibrary.com/mcp/server.py" } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSSE (Remote)
Last updatedAug 1, 2026
11/11 checks healthy over the last 46d
Views1
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
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Last commit1mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 1, 2026
47Quality signal: Fair Β· 47/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools23/30
Adoption & activity2/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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