GooseTeam MCP Server

The GooseTeam MCP server is designed to turn a collection of autonomous agents into a coordinated, task‑oriented team. By exposing a lightweight HTTP interface that follows the Model Context Protocol (MCP), it allows agents built with any LLM to register, exchange messages, and receive structured tasks from a central coordinator. This solves the common pain point of orchestrating multiple agents—each with its own capabilities and state—without requiring custom middleware or bespoke APIs.

At its core, the server provides a set of declarative tools that agents can invoke. Agent registration gives every participant a unique identity and a persistent channel for communication, while message management stores and retrieves inter‑agent chatter so context is never lost. The task management API enables a “Project Coordinator” role to create, assign, and track tasks across the team, effectively turning the MCP into a lightweight workflow engine. Agents can also pause for configurable intervals with agent waiting, ensuring that they do not overwhelm downstream services or each other. Finally, the optional SSE proxy allows many agents to share a single MCP instance, which is essential for collaborative scenarios such as joint research or distributed problem solving.

One of the standout features of GooseTeam is its support for behavioral control via Mermaid markdown. Agents can receive a concise flowchart that dictates their expected actions, making it trivial for LLMs to follow complex protocols without hard‑coded logic. This is demonstrated in the README screenshots, where GPT‑4o accepts a Mermaid chart and behaves accordingly. The visual protocol also aids humans in debugging and refining agent workflows, as the flowchart can be edited directly and re‑loaded by agents on the fly.

Real‑world use cases include project management for software teams, where an AI coordinator assigns code reviews or documentation tasks to specialized agents; scientific research pipelines that require data collection, analysis, and report generation across multiple models; or customer‑support ecosystems where different agents handle triage, knowledge base lookup, and escalation. In each scenario, GooseTeam reduces the overhead of building custom orchestrators, allowing developers to focus on defining agent capabilities rather than plumbing.

Because the server is built on MCP and exposes standard tool endpoints, it integrates seamlessly into existing AI workflows. Developers can hook GooseTeam into any LLM that supports MCP, use the inspector tool to monitor state and tasks in real time, or replace the Goose CLI with another agent framework without changing the server. The result is a flexible, extensible platform that turns isolated AI models into a cohesive, collaborative team—an essential step toward scalable, multi‑agent applications.