MCP-maestro

*Every great research operation needs a conductor.*

This MCP server turns Maestro into a tool your AI assistant can actually direct. Think of it as the bridge between "write me a report" and having a whole research orchestra play in harmony.

Maestro is an AI research framework with serious infrastructure. While others send one agent to do one thing, Maestro coordinates multiple specialized agents — planning, research, writing, reflection — all working together to produce properly structured, multi-section research output.

• Source: github.com/Dianachong/maestro
• Agent count: 5+ specialized agents
• Secret sauce: Agentic layer with planning, reflection, and writing passes

The backend runs an agentic layer on top of multiple LLM calls, manages research cycles, and maintains a proper document pipeline with embeddings and reranking. It's serious research infrastructure.

It exposes Maestro's mission management system through MCP. You can:

• Fire off missions and let Maestro's agents do the heavy lifting
• Track progress in real-time as sections get researched
• Pause, resume, or stop research mid-flight
• Pull reports once the orchestra finishes playing

| Tool | What it does | |------|--------------| | create_mission | Launch a new research mission | | get_report | Pull the research report when done | | get_notes | Get all research notes collected | | resume | Continue a paused mission | | stop | Cancel a running mission |

• Docker and Docker Compose
• Python 3.10+

# Docker compose is the easiest path
git clone https://github.com/Dianachong/maestro.git
cd maestro/docker
docker compose up

This spins up the backend API plus PostgreSQL with pgvector for embeddings.

For more complex setups, check the [offici