dvcrn 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)
# 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
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "smart_search",
"arguments": {
"query": "your topic here",
"mode": "hybrid",
"limit": 5
}
}
}
Save a memory
{
"method": "tools/call",
"params": {
"name": "mem_save",
"arguments": {
"content": "User prefers concise responses with code examples",
"type": "preference",
"project": "my-project"
}
}
}
Create a plan
{
"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:
## 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:
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