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Overview

MCP is an open standard for enabling AI agents to interact with external data and tools. MCPClient provides a way to access and call tools via MCP. For example, instead of writing bespoke function call implementations for an external API, you may use an MCP server that provides a bridge to the API. Be aware there may be security implications. See MCP documenation for more details.

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Installation

To use MCPClient, install the required dependencies:

pip install "pipecat-ai[mcp]"

You may also need to set environment variables as required by the specific MCP server to which you are connecting.

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Configuration

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Constructor Parameters

You can connect to your MCP server via Stdio or SSE transport. See here for more documentation on MCP transports.

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server_params
str | StdioServerParameters
required

You can provide either:

  StdioServerParameters(
        command="python",  # Executable
        args=["example_server.py"],  # Optional command line arguments
        env=None,  # Optional environment variables
    )

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Input Parameters

See more information regarding server params here.

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Usage Example

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MCP Stdio Transport Implementation


# Import MCPClient and StdioServerParameters
...
from mcp import StdioServerParameters
from pipecat.services.mcp_service import MCPClient
...

# Initialize an LLM
llm = ...

# Initialize and configure MCPClient with server parameters
mcp = MCPClient(
        server_params=StdioServerParameters(
            command=shutil.which("npx"),
            args=["-y", "@name/mcp-server-name@latest"],
            env={"ENV_API_KEY": "<env_api_key>"},
        )
    )

# Create tools schema from the MCP server and register them with llm
tools = await mcp.register_tools(llm)

# Create context with system message and tools
# Tip: Let the LLM know it has access to tools from an MCP server by including it in the system prompt.
context = OpenAILLMContext(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant in a voice conversation. You have access to MCP tools. Keep responses concise."
        }
    ],
    tools=tools
)

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MCP SSE Transport Implementation


# Import MCPClient
...
from pipecat.services.mcp_service import MCPClient
...

# Initialize an LLM
llm = ...

# Initialize and configure MCPClient with MCP SSE server url
mcp = MCPClient(server_params="https://your.mcp.server/sse")

# Create tools schema from the MCP server and register them with llm
tools = await mcp.register_tools(llm)

# Create context with system message and tools
# Tip: Let the LLM know it has access to tools from an MCP server by including it in the system prompt.
context = OpenAILLMContext(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant in a voice conversation. You have access to MCP tools. Keep responses concise."
        }
    ],
    tools=tools
)

## Methods

<ResponseField name="register_tools" type="async method">
  Converts MCP tools to Pipecat-friendly function definitions and registers the functions with the llm.

```python
async def register_tools(self, llm) -> ToolsSchema:

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Additional documentation

See MCP’s docs for MCP related updates.