The Living Handbook
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
The Living Handbook
A living article for beginner vibe coders — and an MCP server their agents can read. Suggestions go through probabilistic Jev evaluation, coherent rewriting and explicit human publication.
Live demo · Русский README · Read the article · Русская статья

Why
An agent saying “refactored, optimized, added tests” does not explain whether a small project became better. The handbook helps beginners discuss concrete goals, risks and trade-offs while keeping the final decision with the person.
Related MCP server: wikikit
What works
A Russian/English article: introduction, 15 sections and 55 sources. Browser-language detection and manual switching.
Selection-based suggestions, mobile/keyboard fallback, ChatGPT sign-in and draft restoration after sign-in.
Jev via OpenRouter Decisions API; optional Claude Haiku 5.5 Writer; a separate Jev check of the candidate.
Private author queue, Markdown editor, preview/comparison, explicit publication and rollback as a new revision.
Read-only MCP tools:
get_articleandget_section, optionallanguage: ru | enand pinnedrevision_id. Site and MCP read the same publication.A global four-new-suggestions-per-sliding-hour admission limit; overflow waits in FIFO without model calls. Quote/replacement: 1,500 characters each; reason: 500.
Immutable source revisions/candidates, conflict detection, idempotent publication, encrypted API settings and a daily provider-call cap.
Run locally
Node.js 22.14+ and npm are required; the current checks were run on Node 22.14.0.
npm ci
npm run check
npm run previewOpen http://127.0.0.1:3891/ and /admin. Preview uses a real local SQLite adapter and injects a test author identity. It does not load .env or configure paid providers. Runtime data stays in ignored .sites-runtime/. Never expose this preview adapter as a production server.
Hosting and MCP
The live application runs as a Worker with D1 through Sites. Hosted identity headers, ChatGPT sign-in and the linked cloud schedule are platform integrations, not a self-contained OAuth implementation. See deployment, MCP and architecture. The demo's /mcp endpoint may require separate Sites/OAuth authorization even though the article is public.
The original production project ID, database, keys, schedules and Git history are not included. Register your own Sites project and secrets. For another host, replace the identity and scheduler adapters first.
Validation and limits
The check script builds the Worker and runs artifact validation, MCP/content smoke, editing, queue, locale and translation tests. Provider responses in automated tests are fixtures, not proof of live provider compatibility. The demo has produced a real 16-part English translation through the configured Writer. The first GitHub CI run passed on Linux/Node.js 22: clean installation and the full check suite.
Jev thresholds are provisional. The article is an editorial draft; its English version is AI-generated. Auto-publication is not implemented. A linked hourly cloud task is configured for the demo, but sustained unattended operation has not been independently established. External hosted MCP/OAuth connectivity and full D1 backup/restore remain verification tasks. Compact agent decision cards and the original automated source-selection pipeline are future work.
Contribute and license
Contributing · Roadmap · Security boundaries · License scope.
The article and editorial content use CC BY 4.0. The application code uses MIT; see LICENSE_SCOPE.md for the exact content/code boundary. References retain their own licenses.
This server cannot be deployed
Maintenance
Related MCP Connectors
Read-only search and retrieval for Official Translator's published guides.
Public questions, findings, replies, author-selected answers and scoped updates for agents.
Read, edit and improve shared documents and knowledge with people and agents.
Read and write knowledge-base articles, categories, snippets, glossary, readers, and search data.
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