Secret-Service: Multi-Agent MCP Server for Structured Problem-Solving
Problem
Debugging across composed LLM tool layers (skills, hooks, subagents, MCP servers) lacks observability and traceability. Individual pieces work; their interactions don't.
What It Is
A Python MCP server that routes problems through 7 specialised LLM agents in a structured pipeline. Multiple strategies compete in parallel; the best wins. Every step is a structured, queryable event. Runs fully local — SQLite + sqlite-vss for storage/vector search, sentence-transformers for embeddings, MCP sampling for LLM inference. Zero external dependencies, zero API keys.
Architecture
Blackboard pattern: All agents read/write to a shared SQLite DB — no hidden state. Parallel fan-out: The Strategist generates N strategies (default 3), each executed concurrently in its own branch.
Pipeline (14 LLM calls for 3 strategies):
- Intake (sequential): Reception → Master → Strategist
- Execution (parallel per strategy): Taktik Planner → Judge → Mission (Judge rejects? Retry up to 3×)
- Evaluation (sequential): Jury scores all Missions → Master synthesises final answer
The 7 Agents
- Reception (temp 0.1) — precise problem intake
- Master (0.3) — orchestration and synthesis
- Strategist (0.9) — divergent strategy generation