Using MCP Servers

Marvin agents can connect to Model Context Protocol (MCP) servers, allowing them to access a wide array of external tools and data sources.

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What is MCP?

MCP provides a standardized way for AI agents to interface with tools, resources, prompts etc. If a thing in the world has an MCP server, you can trivially write an agent that interacts with it (on the terms of the MCP server implementation).

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Connecting an Agent to an MCP Server

To give your agent access to an MCP server, you simply pass MCPServer instances to the mcp_servers argument when creating your Agent.

Marvin uses pydantic-ai under the hood for MCP server interactions. The most common server type you’ll likely use is MCPServerStdio, which communicates with an MCP server running as a subprocess via its standard input/output.

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Example: Using a Python Interpreter via MCP

Let’s say you want your agent to be able to run Python code. There are MCP servers that provide a Python interpreter as a tool. The jsr:@pydantic/mcp-run-python package is one such server that can be run with Deno.

from marvin.agents import Agent
from pydantic_ai.mcp import MCPServerStdio

# Configure the MCP server for running Python code
# This requires Deno and the jsr:@pydantic/mcp-run-python package
# You can install the Deno package with: 
# deno install -A -g jsr:@pydantic/mcp-run-python
run_python_mcp_server = MCPServerStdio(
    command="deno", # Or the full path to your deno executable
    args=["run", "-A", "jsr:@pydantic/mcp-run-python", "stdio"],
)

# Create an agent and provide the MCP server
coder_agent = Agent(
    name="Coder",
    instructions="Use the Python interpreter to solve tasks.",
    mcp_servers=[run_python_mcp_server]
)

async def run_code():
    result = await coder_agent.run_async(
        "Use python to calculate the square root of 256 and tell me the result."
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(run_code())

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Example: Using a Git Tool Server via MCP

Another common use case is interacting with Git repositories. The mcp-server-git package (runnable with uvx, part of uv) provides Git tools over MCP.

from marvin.agents import Agent
from pydantic_ai.mcp import MCPServerStdio

# Configure the MCP server for Git tools
# This requires uv and mcp-server-git (installable via: uvx mcp-server-git, if needed)
git_mcp_server = MCPServerStdio(
    command="uvx", 
    args=["mcp-server-git"] 
)

version_control_agent = Agent(
    name="VersionControlPro",
    instructions="Use Git tools to inspect the repository.",
    mcp_servers=[git_mcp_server]
)

async def get_repo_info():
    result = await version_control_agent.run_async(
        "Get the latest commit message from this repository."
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(get_repo_info())

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How it Works

When an agent configured with mcp_servers runs:

1. Marvin discovers the tools provided by each active MCP server.
2. These discovered tools (e.g., run_python_code from the Python server, or git_log from the Git server) are made available to the LLM just like standard Python tools.
3. If the LLM decides to use an MCP tool, Marvin’s engine handles the communication with the specified MCPServer instance.
4. The result from the MCP server is processed and returned to the LLM, which then formulates the final response.

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Exploring Further

For a more comprehensive example demonstrating multiple MCP servers and tools, check out the example script in the Marvin repository:

examples/agent_mcp.py

This example showcases an agent that uses both a Python interpreter and Git tools via MCP to perform a multi-step task. You can run it locally to see MCP integration in action.

This setup allows Marvin agents to flexibly leverage a growing ecosystem of MCP-compatible tools and services.