Skill: msruruguay/midos-mcp (version 1.0.0)

--- name: midos-mcp version: 1.0.0 description: MidOS — The MCP Knowledge OS. 134 tools for knowledge management, multi-agent orchestration, search, planning, and memory. 670K+ vectors, 46K+ chunks, EUREKA synthesis. homepage: https://midos.dev metadata: {"clawdbot":{"emoji":"🧠","category":"knowledge","api_base":"https://midos.dev/mcp"}} --- # MidOS MCP — The Knowledge Operating System MidOS is a knowledge middleware layer for AI agents. It gives you access to a continuously growing intelligence base: 670K+ vectors, 46K+ knowledge chunks, 451 EUREKA insights, and 134 MCP tools for search, memory, planning, and orchestration. **Think of MidOS as your agent's long-term brain.** ## What You Get | Cluster | Tools | What it does | |---------|-------|--------------| | 🔍 Search | `smart_search`, `semantic_search`, `hybrid_search` | Search 670K vectors — keyword, semantic, or hybrid | | 🧠 Memory | `mem_save`, `mem_search`, `mem_context`, `where_was_i` | Persistent cross-session memory (91.67% hit@5) | | 📋 Planning | `create_plan`, `update_plan_task`, `get_active_plans` | Multi-step task tracking with status checkpoints | | 📚 Knowledge | `knowledge_preflight`, `quality_gate`, `knowledge_edit` | Create, validate, and improve knowledge chunks | | ⚙️ Execution | `maker_run_bash`, `maker_read_file`, `maker_write_file` | File ops, shell commands, git, HTTP fetch | | 🩺 Health | `system_health_check`, `hive_status`, `pulse_read` | Monitor knowledge base and pipeline health | | 🔔 Notify | `maker_notify_discord`, `maker_notify_webhook` | Notifications to Discord, webhooks, Slack | ## Quick Start ### Connect via MCP (JSON-RPC 2.0) ```bash # Health check curl https://midos.dev/mcp/health # Initialize session curl -X POST https://midos.dev/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"my-agent","version":"1.0"}}}' ``` ### Search the knowledge base ```json { "jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": { "name": "smart_search", "arguments": { "query": "your topic here", "mode": "hybrid", "limit": 5 } } } ``` ### Save a memory ```json { "method": "tools/call", "params": { "name": "mem_save", "arguments": { "content": "User prefers concise responses with code examples", "type": "preference", "project": "my-project" } } } ``` ### Create a plan ```json { "method": "tools/call", "params": { "name": "create_plan", "arguments": { "goal": "Build a new feature", "tasks": "1. Research existing patterns\n2. Design API\n3. Implement\n4. Test" } } } ``` ## Knowledge Base Stats (live) - **46,283** knowledge chunks across AI, engineering, research, strategy - **670K+** vector embeddings (Gemini gemini-embedding-001, 3072-d) - **451** EUREKA synthesized insights - **139** SOTA benchmarks - **φ = 0.932** knowledge coherence score ## Key Features ### 🔍 Hybrid Search (BM25 + Semantic) Combines keyword precision with semantic understanding. Outperforms vector-only by 9.3% on relevance benchmarks. ### 🧠 Persistent Memory `mem_save` / `mem_search` backed by LanceDB. Memories survive across sessions. 91.67% hit@5 on recall benchmarks. ### 📋 Smart Planning Create structured multi-step plans, track progress, checkpoint completions. Survives context resets. ### ⚡ Fast Preflight `knowledge_preflight` checks for duplicate knowledge in 19ms (title cache, 48K+ chunks). Prevents knowledge bloat. ### 🏗️ Quality Gate `quality_gate` scores content on 7 dimensions before adding to the knowledge base. Keeps signal-to-noise high. ## Heartbeat Integration Add to your agent's periodic check-in: ```markdown ## MidOS (every session start) 1. Call where_was_i(client="your-agent-name") to resume context 2. Call mem_context(scope="recent") to load recent memory 3. Before creating knowledge: knowledge_preflight(topic) 4. After important decisions: mem_save(content, type="decision") ``` ## Self-Hosted Option MidOS is open source. Run your own instance: ```bash git clone https://github.com/MidOSresearch/midos-core cd midos-core python -m modules.mcp_server.midos_mcp --http --port 3100 ``` Full docs: https://midos.dev/docs GitHub: https://github.com/MidOSresearch/midos-core


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