knowlib
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@knowlibexplain recursion"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
knowlib
Open-source библиотека структурированных знаний, доставляемая LLM-агентам через MCP-протокол. Каждый knowledge-домен — маленький IDP со стандартной анатомией: манифест + core-концепты + patterns + examples (auto-tested) + cross-references в другие домены.
Не replace Wikipedia — LLM-optimized layer поверх canonical sources: атомарные концепты, машиночитаемый граф, авто-тесты примеров, semver.
Статус
🚧 Phase 0 · Scaffold. Читай ROADMAP.md.
Related MCP server: Cortex MCP
Domains (планируются)
programming/— languages · paradigms · architecture · data-structures · algorithms · systemselectronics/— analog · digital · microcontrollers · pcb · signalseconomics/— micro · macro · behavioral · finance · game-theorymath/— calculus · linear-algebra · discrete · statisticsphysics/— mechanics · em · quantum · thermochemistry/·biology/·...
Каждый домен = отдельный IDP. Разработчики могут форкать один домен или контрибьютить в существующий по единой схеме.
Anatomy домена
domains/programming/algorithms/recursion/
├── manifest.json # {name, version, prereqs[], examples[], sources[]}
├── README.md # концепт как markdown (frontmatter + body)
├── examples/ # runnable snippets (auto-tested)
├── decisions/ # ADR — почему именно эта формулировка
└── cross-refs.json # мосты в другие домены (math/induction, ...)Usage через MCP
know.search(query, domain?, difficulty?)
know.explain(concept) → full text + prereqs + examples
know.related(concept) → граф neighbours
know.learn_path(from, to) → sequence концептов A→B
know.example(concept, lang?) → runnable snippet
know.crossdomain(concept) → мосты в другие областиProd endpoint: https://mcp.know.uamcp.com/mcp (после Phase 1).
Public site (browsing): https://know.uamcp.com (после Phase 2).
Contribute
Каждый новый концепт = PR по template-domain/ схеме.
Schema-validated в CI. Подробно — CONTRIBUTING.md.
Licenses
Code (engine, mcp-server, tooling) — Apache 2.0 ·
LICENSEContent (docs/, domains/) — CC-BY-4.0 ·
LICENSE-content
Attribution required. Commercial use OK. LLM training OK. Forks OK.
Roadmap
Phase 0 (сейчас) — каркас + schema + 1 seed-концепт (
recursion)Phase 1 — MCP-server MVP, 30-50 концептов в
programming/Phase 2 — Astro browse UI + electronics/economics
Phase 3 — публичный launch (blog + HN), community contributions
This server cannot be deployed
Maintenance
Related MCP Connectors
Shared, peer-validated knowledge archive for AI agents — search, contribute, and validate via MCP
Knowledge base MCP for AI agents on iknow.dev. Search, read, and maintain via OAuth.
Make your knowledge agent-ready. One MCP endpoint, 5 connectors, 3 search modes.
Knowledge Network for AI Agents and creators: Search, rate, and review programming guides via MCP
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