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MCP Community Library — Curated knowledge, skill packs, and semantic search for AI coding agents

Project description

MidOS — MCP Server for Developer Knowledge

Curated, validated knowledge for AI coding agents. Not raw docs — battle-tested patterns.

MCP Compatible Claude Code Cursor Cline Windsurf
MIT License GitHub stars Smithery Python 3.10+


104 skill packs across 20+ tech stacks. 1,284 curated chunks. 104 validated discoveries. Every piece reviewed, cross-validated, and myth-busted.

Your agent asks: "How do I implement optimistic updates in React 19?"
MidOS returns: Battle-tested pattern with useOptimistic + Server Actions, validated Feb 2026.
Context7 returns: Raw React docs from reactjs.org.

Quick Start

One line. Add to your MCP config and start querying:

Claude Code.mcp.json or ~/.claude/settings.json
{
  "mcpServers": {
    "midos": {
      "url": "https://midos.dev/mcp"
    }
  }
}
Cursor / Windsurf — MCP Settings

Add a new server:

  • Name: midos
  • URL: https://midos.dev/mcp
  • Transport: Streamable HTTP
Cline — MCP Settings
{
  "mcpServers": {
    "midos": {
      "url": "https://midos.dev/mcp",
      "transportType": "streamable-http"
    }
  }
}
Self-hosted — Run locally
git clone https://github.com/MidOSresearch/midos-mcp.git
cd midos-mcp
pip install -e .
pip install -e hive_commons/
python -m modules.mcp_server.midos_mcp --http --port 8419

Then point your MCP client to http://localhost:8419/mcp.

First Tool Call

After connecting, personalize your experience:

agent_handshake(model="claude-opus-4-6", client="claude-code", languages="python,typescript", frameworks="fastapi,react")

Then search for what you need:

search_knowledge("React 19 Server Components patterns")

Tools Reference

Community Tier (free, no API key)

Tool Description Example
search_knowledge Search 1,284 curated chunks across all stacks search_knowledge("FastAPI dependency injection")
hybrid_search Combined keyword + semantic search with reranking hybrid_search("PostgreSQL JSONB indexing")
list_skills Browse 104 skill packs by technology list_skills(stack="react")
get_skill Get a specific skill pack (preview in free, full in Dev) get_skill("nextjs")
get_protocol Protocol and pattern documentation get_protocol("domain-driven-design")
hive_status System health and live statistics hive_status()
project_status Knowledge pipeline dashboard project_status()
agent_handshake Personalized onboarding for your model + stack See example above

Dev Tier ($19/mo — full content + advanced search)

Tool Description Example
get_eureka Validated breakthrough discoveries (104 items) get_eureka("response-cache")
get_truth Empirically verified truth patches (17 items) get_truth("qlora-myths")
semantic_search Vector search with Gemini embeddings (3072-d) semantic_search("event sourcing CQRS")
research_youtube Extract knowledge from video content research_youtube("https://youtube.com/...")
chunk_code Intelligent code chunking for ingestion chunk_code(code="...", language="python")
memory_stats Vector store analytics and health memory_stats()
episodic_search Search agent session history episodic_search("last deployment issue")

Ops Tier (custom — security, infrastructure, advanced ops)

Contact for specialized knowledge packs. midos.dev/pricing

Skill Packs (104 and growing)

Production-tested patterns for:

Frontend: React 19, Next.js 16, Angular 21, Svelte 5, Tailwind CSS v4, Remix v2

Backend: FastAPI, Django 5, NestJS 11, Laravel 12, Spring Boot, Symfony 8

Languages: TypeScript, Go, Rust, Python

Data: PostgreSQL, Redis, MongoDB, Elasticsearch, LanceDB, Drizzle ORM, Prisma 7

Infrastructure: Kubernetes, Terraform, Docker, GitHub Actions

AI/ML: LoRA/QLoRA, MCP patterns, multi-agent orchestration, Vercel AI SDK

Testing: Playwright, Vitest

Architecture: DDD, GraphQL, event-driven, microservices, spec-driven dev

How MidOS is Different

Raw Docs (Context7, etc.) MidOS
Content Documentation dumps Curated, human-reviewed, cross-validated
Quality No validation 5-layer pipeline: chunks → truth → EUREKA → SOTA
Search Keyword matching Semantic + hybrid search (Gemini embeddings, 3072-d)
Onboarding Generic Personalized per model + CLI + stack
Format Raw text Stack-specific skill packs with production patterns
Accuracy Stale docs Myth-busted with empirical evidence

Knowledge Pipeline

staging/ → chunks/ → skills/ → truth/ → EUREKA/ → SOTA/
 (entry)    (L1)      (L2)      (L3)     (L4)      (L5)
  • Chunks (1,284): Curated, indexed knowledge across 20+ stacks
  • Skills (104): Organized, actionable, versioned by stack
  • Truth (17): Verified with empirical evidence
  • EUREKA (104): Validated improvements with measured ROI
  • SOTA (11): Best-in-class, currently unimprovable

Using an API Key

Pass your key via the Authorization header for Dev/Ops access:

{
  "mcpServers": {
    "midos": {
      "url": "https://midos.dev/mcp",
      "headers": {
        "Authorization": "Bearer midos_your_key_here"
      }
    }
  }
}

Get a key at midos.dev/pricing.

Architecture

midos-mcp/
├── modules/mcp_server/   FastMCP server (streamable-http)
├── knowledge/
│   ├── chunks/            Curated knowledge (L1) — 1,284 items
│   ├── skills/            Stack-specific skill packs (L2) — 104 items
│   ├── EUREKA/            Validated discoveries (L4) — 104 items
│   └── truth/             Empirical patches (L3) — 17 items
├── hive_commons/          Shared library (LanceDB vector store, config)
├── smithery.yaml          Smithery marketplace manifest
├── Dockerfile             Production container
└── pyproject.toml         Dependencies and build config

Tech Stack

  • Server: FastMCP 2.x (streamable-http transport)
  • Vectors: LanceDB + Gemini embeddings (22,900+ vectors, 3072-d)
  • Auth: 3-tier API key middleware (community → dev → ops) with rate limiting
  • Pipeline: 5-layer quality validation with myth-busting
  • Deploy: Docker + Coolify (auto-deploy on push)

Contributing

MidOS is community-first. If you have production-tested patterns, battle scars, or discovered that a popular claim is false — we want it.

  1. Search existing knowledge first: search_knowledge("your topic")
  2. Open an issue describing the pattern or discovery
  3. We'll review and add it to the pipeline

License

MIT


Source-verified developer knowledge. Built by devs, for agents.
midos.dev · Discussions · Issues

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