Skip to main content

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

midos_mcp-2026.2.23.tar.gz (915.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

midos_mcp-2026.2.23-py3-none-any.whl (69.1 kB view details)

Uploaded Python 3

File details

Details for the file midos_mcp-2026.2.23.tar.gz.

File metadata

  • Download URL: midos_mcp-2026.2.23.tar.gz
  • Upload date:
  • Size: 915.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for midos_mcp-2026.2.23.tar.gz
Algorithm Hash digest
SHA256 32868f3422d4d3ecb33bd32d4e7ded0d4c54ede67aeed41902b3d484563c12dd
MD5 4c428c7184f38fb1473a28a99f9acf69
BLAKE2b-256 2338beed5e8ba1019897df07de2be451a4b708b2dd7788b08847a1f5a7daafe4

See more details on using hashes here.

File details

Details for the file midos_mcp-2026.2.23-py3-none-any.whl.

File metadata

  • Download URL: midos_mcp-2026.2.23-py3-none-any.whl
  • Upload date:
  • Size: 69.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for midos_mcp-2026.2.23-py3-none-any.whl
Algorithm Hash digest
SHA256 79e9e4f1e0f6317c2c5f4603d917482530ad68424ad939614844c39970476dad
MD5 5df5a09f7ded94d598afc09e4aa86ce7
BLAKE2b-256 376402b64339be2076b1725cff75d2a55b8099561266ee949f891f8a09e433de

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page