--- slug: "gooseteam-mcp服务器" source_type: "readme" source_url: "https://cdn.jsdelivr.net/gh/cliffhall/GooseTeam@main/README.md" repo: "https://github.com/cliffhall/GooseTeam" source_file: "README.md" branch: "main" --- # GooseTeam ![GooseTeam Logo](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/GooseTeam.png) ## Enabling Goose Agents to Collaborate ### **Protocol:** An operational procedure for agents to follow, expressed in two flavors: - [**Natural Language**](https://github.com/cliffhall/GooseTeam/blob/HEAD/instructions/protocol-as-text.md): Streamlined, human-readable instructions about how to behave and use the tools. - [**Mermaid Markdown**](https://github.com/cliffhall/GooseTeam/blob/HEAD/instructions/protocol-as-chart.md): A concise flowchart for expected behavior, described in an efficient text format. ### **Extension:** Goose extension ([MCP Server](https://github.com/modelcontextprotocol)) that provides tools for the agents to use for tasks and messaging. - **Agent Registration:** Facilitates agent registration with unique ID assignments and message sending. - **Message Management:** Stores messages from agents, making them available for retrieval. - **Task Management:** Project Coordinator role creates and assigns tasks and roles to other agents. - **Agent Waiting:** Allows connected agents to wait for a specified period before taking another action. - **Remote Server:** With an MCP Proxy, multiple agents can connect to the same MCP server, necessary for collaboration. ### Work in Progress * Current focus is on [Goose CLI](https://block.github.io/goose/docs/getting-started/installation/) integration, so you must have it installed. * Other agent platforms could integrate with this project easily. * The protocol and the MCP server are not tightly coupled to goose, only the npm `agent:` scripts and in the protocol preamble. ## NEW: Behavioral Control with Mermaid markdown! * Agent protocol expressed as a [mermaid chart](https://mermaid.live/edit#pako:eNp1VG1v2jAQ_isnf-kX2hVKAkRTJwSbhjRa1FJVW0CVl5gQQWxmO3Rdyn_f2YkTyrp8iM5399w992IXJBIxIwFJJN2tYT5ecMDvXlOpQ_tfwvn5NdyxJFWaydAJQBPG9cef8sO1FpAwDZPxsgTXLgY4ZihlKWezUVjLFhblUmIImI0QVyKPnC14omajYaTTPSsmCvSaSQaUWzS1agR_OpTYxtdAX2_EK8zpht3ukbQRQOwNa4UQG-Ce8TiAswnQDLh4NtHRdLZ8N9p3pl7hmxA7x9SFtjSNIfzMTclbFOtyjN46jNYs2kyZUtg0FdoTSBa5BmaVpcr9xtviZ1JEeK4jVGdwwMCGOYeHXUw1g-wFNFWbSnkjULXOM8oh5UrLHMsSXL2FYMOVpjpXkK4gWlOesLiu4yR9NZopTqkYZjCpWtcMYlrNr2zblHJEzZGQCkvZsqtJu2L-S3GoVJrwEgS4bHbznPGOqZ3gsdFL9itnSqvlPzzMMnyle0uiMAJUy1fuL2oN-RLm_JoCHoXc3HKjC40IouSyfMffJBqqzRchrTuKprMrIZuBVHs32zKqcJvL2jKUrMtZ3fQma9Xvkch2W7wgxdyFiipN03nn05B3GstnSpG-SQMOauM8KBbUmqej2k4CmuoeaapD84MOKBZh61UTw07m6dla60KOGdhSDLo0Na06MRztzInFpnaXjrRIhg8GTWN8wQrjsCC4jBlbkADFmK1ovtULsuAHdKW5FvcvPCIBbhhrESnyZE2CFd0qPOX2IoxTii9hVmt3lP8QInMQPJKgIL9J0O56F12v3en6V_1u3-u3ey3yQgJ_cNG76vg9r-O3_atev3dokT82wOWF1-l2PG_g9QcD37v0-4e_wqjSEw). * See if your favorite LLM can follow it using the [`agent:chart`](#agent---protocol-as-chart) script to launch your agent! ## Screenshots ### GooseTeam Protocol as a Chart ![goose-protocol-chart.png](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/goose-team-protocol-chart.png) ### GPT-4o Says OK to this as a control mechanism ![Goose PC Follows Mermaid](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/mermaid-ok-sez-gpt-4o.png) ### Goose Agents ![Goose Agents](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/goose-agents.png) ### Inspector - List Tools ![Inspector](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/inspector.png) ### Inspector - Add Message ![inspector-add-message.png](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/inspector-add-message.png) ### Inspector - List Tasks ![inspector-list-tasks.png](https://github.com/cliffhall/GooseTeam/raw/HEAD/images/inspector-list-tasks.png) ## How to Run ### Tell Goose about the extension * Run `goose configure` * Choose **[Add Extension]()** * Choose **Remote Extension** * Enter `goose-team` for name * Enter http://localhost:3001/sse for SSE endpoint URI ### Run these npm scripts in order: * First, clone this repo to your local machine and [install dependencies](#install-dependencies) * `npm run build` - to build the server from source. * `npm run mcp-proxy` to start an SSE proxy to talk to a single instance of the goose-team MCP server, * `npm run inspector` to launch the MCP inspector in a browser * Here you click **connect**, then `list_tools`, then `add_message`, then from `Human` send a message describing a task or project for the team to work on. * `npm run agent` to launch an agent that will read the protocol and instructions, connect to the `goose-team` server, assign itself as project coordinator, then begin creating tasks. * In the inspector window, click `list_tasks` to see the tasks it is adding, * When there are some tasks, you can `npm run agent` again and again to launch agents to be assigned tasks by the Project Coordinator. ## Troubleshooting ### HOW WELL GOOSE TEAM WORKS DEPENDS ON THE MODEL!!! Follow these steps to test if you have a model that will work. * `npm run build` - to build the server from source. * `npm run mcp-proxy` to start an SSE proxy to talk to a single instance of the goose-team MCP server, * `npm run agent:test` This launches an agent hitting the same MCP, but it has a simpler protocol: Say hello in a different language, wait 2 seconds and do it again. * [Here is a video](https://youtu.be/we1ESnafqy4?si=mg2bxQ1k4SePf5yh) of a successful `agent:test` run. Currently, the only models I can get to stay in the loop with are `google/gemini-2.0-flash-001` (via [openrouter.ai](https://openrouter.ai) to avoid rate limit shutdowns) and `openai/gpt-4o`. Others will fall out of the loop. It is important for the model to follow the protocol and stay in the loop, checking messages, sleeping, particularly for the Project Coordinator. I suggest doing `build`,` mcp-proxy`, and `agent:test` and if the model you have configured does some work then stops with a "message loop ended" error, you have a model that just isn't very good at using tools. Try different models and see what works. If you get anything other than the above-mentioned models to actually stay on the job, then please let me know! ## MCP Tools ### Agents - **`register_agent`** - Registers a new agent and provides a unique ID and a randomly assigned color. - **Inputs:** None - **Returns:** JSON agent with unique `id` and assigned `color`. - **`list_agents`** - Lists all registered agents, providing an overview of available participants. - **Inputs:** None - **Returns:** JSON list of all agents connected to the server. - **`agent_leave`** - Allows an agent to leave the team. - **Inputs:** None. - **Returns:** JSON of the agent that left. - **`agent_wait`** - Allows an agent to wait for a specified number of seconds to pass before performing another action. - **Inputs:** - `seconds`: the number of seconds to wait. - **Returns:** Confirmation of time elapsed. ### Messages - **`add_message`** - Allows an agent to send a new message, storing it within the server. - **Inputs:** - `senderId`: ID of the agent sending the message. - `content`: Content of the message. - **Returns:** Confirmation of message addition. - **`recent_messages`** - Retrieves the most recent messages stored on the server. - **Inputs:** None - **Returns:** JSON array containing the three most recent messages. - **`list_messages`** - Retrieves all messages stored on the server. - **Inputs:** None - **Returns:** JSON array containing all messages in the server. - **`clear_messages`** - Clears all messages stored on the server. - **Inputs:** None - **Returns:** Confirmation of messages cleared. ### Tasks - **`add_task`** - Add a task to the server. - **Inputs:** - `description`: Description of the task. - **Returns:** The newly added task, with it's assigned id. - **`list_tasks`** - Lists all tasks in the server. - **Inputs:** None - **Returns:** JSON list of all tasks in the server. - **`assign_task`** - Assign a task to an agent. - **Inputs:** - `taskId`: ID of the task. - `agentId`: ID of the agent to assign the task to. - **Returns:** Confirmation of message addition. - **`complete_task`** - Complete a task. - **Inputs:** - `taskId`: ID of the task. - **Returns:** Confirmation of task completion. ## Developer Setup ### Install Dependencies - `cd /path/to/GooseTeam/` - `npm install` ### Build - `npm run build` - Builds the stdio-based MCP server runtime at `/dist/index.js` ### STDIO Server - Proxied - `npm run stdio:proxy` - Launches an SSE-based/MCP proxy on port `:3001` with endpoint `/sse` - This proxy talks to a single instance of the STDIO-wrapped GooseTeam MCP server - Multiple clients can connect to the same server and see the same resources ### SSE Server - Direct - `npm run sse:direct` - Launches an SSE-wrapped instance of the GooseTeam MCP server on port `:3001` with endpoint `/sse` - Multiple clients can connect to the same server and see the same resources - Equivalent to the proxied STDIO approach, using our SSE version of the server. ### StreamableHTTP Server - Direct - `npm run streamableHttp:direct` - Launches a StreamableHTTP-wrapped instance of the GooseTeam MCP server on port `:3001` with endpoint `/mcp` - Multiple clients can connect to the same server and see the same resources - Equivalent to the proxied STDIO approach, using our SSE version of the server. ### Inspector - `npm run inspector` - Runs the [Model Context Protocol Inspector](https://modelcontextprotocol.io/docs/tools/inspector) - The Inspector UI will be available at: http://localhost:5173 - In the Inspector UI: - Make sure `Transport Type` is set to `StreamableHttp` - Make sure `URL` is set to http://localhost:3001/mcp - Click its **"Connect"** button - You should see Green light 🟢and **"Connected"** message. - Click its **List Tools** button - Multiple instances of the client can connect to the same server and share the same resources ### Agent - Protocol as Text - `npm run agent:text` - Starts a new GooseTeam agent, with its waddling orders given in: `instructions/protocol-as-text.md` - This agent's protocol instructions are expressed in text markdown format, with headers, text, and bullets. - NOTE: It is a natural language prompt, but because it requires waiting and staying in a loop, some LLMs may balk - If the LLM won't stay in the loop, try the [Agent Wait Test](#agent-wait-test) with different LLMs to find one that is suitable. - First agent will assume Project Coordinator Role - **NOTE:** It's best to connect to the server with the Inspector BEFORE launching the first agent - Send a message from "Human" telling it what you'd like the team to accomplish ### Agent - Protocol as Chart - `npm run agent:chart` - Starts a new GooseTeam agent, with its waddling orders given in: `instructions/protocol-as-chart.md` - This agent's protocol instructions are expressed in mermaid markdown format, a compact way for expressing flowcharts. - NOTE: although this is an efficient way of expressing the protocol, some LLMs may not understand it - If the LLM doesn't understand mermaid markdown, it will say so and quit. - First agent will assume Project Coordinator Role - **NOTE:** It's best to connect to the server with the Inspector BEFORE launching the first agent - Send a message from "Human" telling it what you'd like the team to accomplish ### Agent Wait Test - `npm run agent:wait:test` - Starts a new GooseTeam agent, with its waddling orders given in: `instructions/wait-loop-test.md` - This will test the configured model's ability to stay in the loop, checking messages periodically. - If it ends with an error saying "outgoing message queue empty" then it is not a good tool use model and therefore a poor candidate for use with GooseTeam. - **NOTE:** Make sure to have the MCP Proxy running first. ### Format - `npm run format` - Runs `prettier` on the code, adjusting formatting ### Typecheck - `npm run typecheck` - Runs `tsc` with args to check and report type issues ### Lint - `npm run lint` - Runs `eslint` to non-destructively check for and report syntax problems ### LintFix - `npm run lint:fix` - Runs `eslint` to check for and fix syntax problems ### Test - `npm run test` - Run the unit tests ## Links - **MCP Specification:** The complete Model Context Protocol specifications can be found [here](https://github.com/modelcontextprotocol/specification/tree/main). - **Server Reference:** We follow a simple but modular approach demonstrated in the [GitHub MCP server](https://github.com/cliffhall/GooseTeam/tree/HEAD/../mcp-servers/src/github). - **Inspiration:** Iterating from Aaron Goldsmith's Gist [here](https://gist.github.com/AaronGoldsmith/114c439ae67e4f4c47cc33e829c82fac). - Watch Aaron's "[Building a team of AI agents](https://www.youtube.com/watch?v=9HJy4uqMW74)" talk about his initial experiment.