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/mcp [BETA] - Model Context Protocol

Expose MCP tools on LiteLLM Proxy Server​

This allows you to define tools that can be called by any MCP compatible client. Define your mcp_servers with LiteLLM and all your clients can list and call available tools.

LiteLLM MCP Architecture: Use MCP tools with all LiteLLM supported models

How it works​

LiteLLM exposes the following MCP endpoints:

  • /mcp/tools/list - List all available tools
  • /mcp/tools/call - Call a specific tool with the provided arguments

When MCP clients connect to LiteLLM they can follow this workflow:

  1. Connect to the LiteLLM MCP server
  2. List all available tools on LiteLLM
  3. Client makes LLM API request with tool call(s)
  4. LLM API returns which tools to call and with what arguments
  5. MCP client makes MCP tool calls to LiteLLM
  6. LiteLLM makes the tool calls to the appropriate MCP server
  7. LiteLLM returns the tool call results to the MCP client

Usage​

1. Define your tools on under mcp_servers in your config.yaml file.​

LiteLLM allows you to define your tools on the mcp_servers section in your config.yaml file. All tools listed here will be available to MCP clients (when they connect to LiteLLM and call list_tools).

config.yaml
model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: sk-xxxxxxx

mcp_servers:
  {
    "zapier_mcp": {
      "url": "https://actions.zapier.com/mcp/sk-akxxxxx/sse"
    },
    "fetch": {
      "url": "http://localhost:8000/sse"
    }
  }

2. Start LiteLLM Gateway​

Docker Run
docker run -d \
  -p 4000:4000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  --name my-app \
  -v $(pwd)/my_config.yaml:/app/config.yaml \
  my-app:latest \
  --config /app/config.yaml \
  --port 4000 \
  --detailed_debug \

3. Make an LLM API request​

In this example we will do the following:

  1. Use MCP client to list MCP tools on LiteLLM Proxy
  2. Use transform_mcp_tool_to_openai_tool to convert MCP tools to OpenAI tools
  3. Provide the MCP tools to gpt-4o
  4. Handle tool call from gpt-4o
  5. Convert OpenAI tool call to MCP tool call
  6. Execute tool call on MCP server
MCP Client List Tools
import asyncio
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionUserMessageParam
from mcp import ClientSession
from mcp.client.sse import sse_client
from litellm.experimental_mcp_client.tools import (
    transform_mcp_tool_to_openai_tool,
    transform_openai_tool_call_request_to_mcp_tool_call_request,
)


async def main():
    # Initialize clients
    
    # point OpenAI client to LiteLLM Proxy
    client = AsyncOpenAI(api_key="sk-1234", base_url="http://localhost:4000")

    # Point MCP client to LiteLLM Proxy
    async with sse_client("http://localhost:4000/mcp/") as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # 1. List MCP tools on LiteLLM Proxy
            mcp_tools = await session.list_tools()
            print("List of MCP tools for MCP server:", mcp_tools.tools)

            # Create message
            messages = [
                ChatCompletionUserMessageParam(
                    content="Send an email about LiteLLM supporting MCP", role="user"
                )
            ]

            # 2. Use `transform_mcp_tool_to_openai_tool` to convert MCP tools to OpenAI tools
            # Since OpenAI only supports tools in the OpenAI format, we need to convert the MCP tools to the OpenAI format.
            openai_tools = [
                transform_mcp_tool_to_openai_tool(tool) for tool in mcp_tools.tools
            ]

            # 3. Provide the MCP tools to `gpt-4o`
            response = await client.chat.completions.create(
                model="gpt-4o",
                messages=messages,
                tools=openai_tools,
                tool_choice="auto",
            )

            # 4. Handle tool call from `gpt-4o`
            if response.choices[0].message.tool_calls:
                tool_call = response.choices[0].message.tool_calls[0]
                if tool_call:

                    # 5. Convert OpenAI tool call to MCP tool call
                    # Since MCP servers expect tools in the MCP format, we need to convert the OpenAI tool call to the MCP format.
                    # This is done using litellm.experimental_mcp_client.tools.transform_openai_tool_call_request_to_mcp_tool_call_request
                    mcp_call = (
                        transform_openai_tool_call_request_to_mcp_tool_call_request(
                            openai_tool=tool_call.model_dump()
                        )
                    )

                    # 6. Execute tool call on MCP server
                    result = await session.call_tool(
                        name=mcp_call.name, arguments=mcp_call.arguments
                    )

                    print("Result:", result)


# Run it
asyncio.run(main())

LiteLLM Python SDK MCP Bridge​

LiteLLM Python SDK acts as a MCP bridge to utilize MCP tools with all LiteLLM supported models. LiteLLM offers the following features for using MCP

  • List Available MCP Tools: OpenAI clients can view all available MCP tools
    • litellm.experimental_mcp_client.load_mcp_tools to list all available MCP tools
  • Call MCP Tools: OpenAI clients can call MCP tools
    • litellm.experimental_mcp_client.call_openai_tool to call an OpenAI tool on an MCP server

1. List Available MCP Tools​

In this example we'll use litellm.experimental_mcp_client.load_mcp_tools to list all available MCP tools on any MCP server. This method can be used in two ways:

  • format="mcp" - (default) Return MCP tools
    • Returns: mcp.types.Tool
  • format="openai" - Return MCP tools converted to OpenAI API compatible tools. Allows using with OpenAI endpoints.
    • Returns: openai.types.chat.ChatCompletionToolParam
MCP Client List Tools
# Create server parameters for stdio connection
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
import os
import litellm
from litellm import experimental_mcp_client


server_params = StdioServerParameters(
    command="python3",
    # Make sure to update to the full absolute path to your mcp_server.py file
    args=["./mcp_server.py"],
)

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        # Initialize the connection
        await session.initialize()

        # Get tools
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")
        print("MCP TOOLS: ", tools)

        messages = [{"role": "user", "content": "what's (3 + 5)"}]
        llm_response = await litellm.acompletion(
            model="gpt-4o",
            api_key=os.getenv("OPENAI_API_KEY"),
            messages=messages,
            tools=tools,
        )
        print("LLM RESPONSE: ", json.dumps(llm_response, indent=4, default=str))

2. List and Call MCP Tools​

In this example we'll use

  • litellm.experimental_mcp_client.load_mcp_tools to list all available MCP tools on any MCP server
  • litellm.experimental_mcp_client.call_openai_tool to call an OpenAI tool on an MCP server

The first llm response returns a list of OpenAI tools. We take the first tool call from the LLM response and pass it to litellm.experimental_mcp_client.call_openai_tool to call the tool on the MCP server.

How litellm.experimental_mcp_client.call_openai_tool works​

  • Accepts an OpenAI Tool Call from the LLM response
  • Converts the OpenAI Tool Call to an MCP Tool
  • Calls the MCP Tool on the MCP server
  • Returns the result of the MCP Tool call
MCP Client List and Call Tools
# Create server parameters for stdio connection
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
import os
import litellm
from litellm import experimental_mcp_client


server_params = StdioServerParameters(
    command="python3",
    # Make sure to update to the full absolute path to your mcp_server.py file
    args=["./mcp_server.py"],
)

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        # Initialize the connection
        await session.initialize()

        # Get tools
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")
        print("MCP TOOLS: ", tools)

        messages = [{"role": "user", "content": "what's (3 + 5)"}]
        llm_response = await litellm.acompletion(
            model="gpt-4o",
            api_key=os.getenv("OPENAI_API_KEY"),
            messages=messages,
            tools=tools,
        )
        print("LLM RESPONSE: ", json.dumps(llm_response, indent=4, default=str))

        openai_tool = llm_response["choices"][0]["message"]["tool_calls"][0]
        # Call the tool using MCP client
        call_result = await experimental_mcp_client.call_openai_tool(
            session=session,
            openai_tool=openai_tool,
        )
        print("MCP TOOL CALL RESULT: ", call_result)

        # send the tool result to the LLM
        messages.append(llm_response["choices"][0]["message"])
        messages.append(
            {
                "role": "tool",
                "content": str(call_result.content[0].text),
                "tool_call_id": openai_tool["id"],
            }
        )
        print("final messages with tool result: ", messages)
        llm_response = await litellm.acompletion(
            model="gpt-4o",
            api_key=os.getenv("OPENAI_API_KEY"),
            messages=messages,
            tools=tools,
        )
        print(
            "FINAL LLM RESPONSE: ", json.dumps(llm_response, indent=4, default=str)
        )