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How-To: Provide tools with Model Context Protocol (MCP)#

The Model Context Protocol (aka MCP) is a way to provide tools and context to the LLM. From the MCP docs:

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

Ragbits has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents.

MCP servers#

Currently, the MCP spec defines three kinds of servers, based on the transport mechanism they use:

  • stdio servers run as a subprocess of your application. You can think of them as running "locally".
  • HTTP over SSE servers run remotely. You connect to them via a URL.
  • Streamable HTTP servers run remotely using the Streamable HTTP transport defined in the MCP spec.

You can use the MCPServerStdio, MCPServerSse, and MCPServerStreamableHttp classes to connect to these servers.

For example, this is how you'd use the official MCP filesystem server (before running, make sure you have Node.js installed on your machine).

from ragbits.agents.mcp import MCPServerStdio

async with MCPServerStdio(
    params={
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-filesystem", "."],
    }
) as server:
    tools = await server.list_tools()

A complete list of working examples is available here.

Using MCP servers#

MCP servers can be added to Agents. Ragbits will call list_tools() on the MCP servers each time the Agent is run. This makes the LLM aware of the MCP server's tools. When the LLM calls a tool from an MCP server, Ragbits calls call_tool() on that server.

from ragbits.agents import Agent
from ragbits.agents.mcp import MCPServerStdio
from ragbits.core.llms import LiteLLM

async with MCPServerStdio(
    params={
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-filesystem", "."],
    }
) as server:
    agent = Agent(llm=LiteLLM(model_name="gpt-4.1-nano"), mcp_servers=[server])
    await agent.run("List all files in the current directory.")

A complete example for local MCP integration is available here.

Caching#

Every time an Agent runs, it calls list_tools() on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass cache_tools_list=True to MCPServerStdio, MCPServerSse, and MCPServerStreamableHttp. You should only do this if you're certain the tool list will not change.

If you want to invalidate the cache, you can call invalidate_tools_cache() on the servers.