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MidOSresearch

MidOS Research Protocol

by MidOSresearch
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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.

Install

pip install midos

Quick Start

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

{ "mcpServers": { "midos": { "url": "https://midos.dev/mcp" } } }

Add a new server:

  • Name: midos

  • URL: https://midos.dev/mcp

  • Transport: Streamable HTTP

{ "mcpServers": { "midos": { "url": "https://midos.dev/mcp", "transportType": "streamable-http" } } }
git clone https://github.com/MidOSresearch/midos.git cd midos 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/ ├── 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

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

Resources

Tools

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