io.github.MidOSresearch/midos-mcp

Search the Midos knowledge base for relevant information. Args: query: Search query (keywords or topic) max_results: Maximum results to return (default: 5)

Get a specific skill/capability document by name. Args: name: Skill name (e.g., 'RAG_SYSTEMS_2026_SOTA')

List all available skills/capabilities. Args: filter: Optional filter for skill names stack: Optional stack filter, comma-separated (e.g. 'python,react'). Skills with matching compatibility.json are prioritized.

Get a specific protocol document. Args: name: Protocol name (e.g., 'PROTOCOL_NEURAL_LINK')

Get a specific EUREKA breakthrough document. Args: name: EUREKA name (e.g., 'EUREKA_CACHE_SEMANTICA' or 'ATOM_001')

Get a specific truth patch document. Args: name: Truth patch name (e.g., 'AGENT_MITIGATIONS_CONTEXT_OVERFLOW')

Get current status of the Midos hive system.

Semantic search using LanceDB vectors (Gemini embeddings). More intelligent than keyword search. Args: query: Natural language query (e.g., 'how to implement RAG pipelines') top_k: Number of results (default: 5) stack: Optional stack filter, comma-separated (e.g. 'python,fastapi'). Results mentioning these are boosted.

Queue a YouTube video for research. Midos will transcribe and extract insights. Args: url: YouTube URL to research priority: Priority: 'high', 'normal', 'low'

Get statistics about the Midos memory system (LanceDB chunks, cache status).

Signal an action to the multi-instance coordination pool. Args: action: Action type: 'completed', 'blocked', 'claimed', 'signaling' topic: Topic/task name summary: Brief description of the action affects: Files/resources affected (optional)

Get multi-instance coordination pool status and recent activity.

Search episodic memory for similar past experiences using vector similarity. Args: query: Search query describing the experience/task limit: Maximum results (default: 5)

Store a new episodic memory/reflection for future learning. Args: task_type: Type of task: CODE, RESEARCH, DEBUG, REVIEW input_preview: Brief description of the input/context success: Whether the task was successful

Parse code file into semantic chunks (functions, classes, methods) for better RAG retrieval. Args: file_path: Path to code file to chunk

Personalized agent onboarding. Declare your environment and get optimal config. Call this FIRST when connecting to MidOS. Pass as much info as you know. Unknown fields can be left empty -- you'll get sensible defaults. Args: model: Your model ID (e.g. 'claude-opus-4-6', 'gemini-2.5-pro', 'opus') context_window: Your context window in tokens (e.g. 200000). 0 = auto-detect from model. client: Your CLI/IDE (e.g. 'claude-code', 'cursor', 'windsurf', 'cline') languages: Comma-separated languages (e.g. 'python,typescript') frameworks: Comma-separated frameworks (e.g. 'fastapi,react') platform: Your OS (e.g. 'windows', 'linux', 'macos') project_goal: What you're working on (e.g. 'manga engine with SVG rendering')

Live MidOS system status + quick-start guide for your agent. Call this anytime to get: - Real-time knowledge base stats (chunks, skills, EUREKA, truth patches) - Vector store health - Available MCP tools with usage examples - Research queue (pending topics) - Tips to get the most out of MidOS This is your /status command. Use it to orient yourself and teach your agent how to leverage MidOS.

[DEPRECATED -- use agent_handshake instead] Generic agent onboarding. Returns default config for unknown agents.

Ask MidOS knowledge base a question. Returns matches from knowledge + EUREKA.

Search MidOS knowledge base for a topic. Returns ranked results.

Search only EUREKA validated knowledge (high-quality findings).

Submit a task to MidOS inbox for async processing.

List all available MidOS skills.

Get the MidOS system architecture map.

Agent onboarding. Returns identity, architecture, available tools, skills, guardrails, and quick-start commands. Call this FIRST when connecting to MidOS.