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Overview

MCP provides a mechanism for Chainlit applications to connect to either server-sent events (SSE) based services or command-line (stdio) based tools. Once connected, your application can discover available tools, execute them, and integrate their responses into your application’s flow.

Chainlit MCP Cookbook

End to end cookbook example showcasing MCP tool calling with Claude.

Connect to an MCP server

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Connections Types

Chainlit supports two types of MCP connections:
  1. SSE (Server-Sent Events): Connect to a remote service via HTTP
  2. stdio: Execute a local command and communicate via standard I/O
⚠️ Security Warning: The stdio connection type spawns actual subprocesses on the Chainlit server. Only use this with trusted commands in controlled environments. Ensure proper validation of user inputs to prevent command injection vulnerabilities.
Command Availability Warning: When using the stdio connection type with commands like npx or uvx, these commands must be available on the Chainlit server where the application is running. The subprocess is executed on the server, not on the client machine.

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Server-Side Configuration (config.toml)

You can control which MCP connection types are enabled globally and restrict allowed stdio commands by modifying your project’s config.toml file (usually located at the root of your project or .chainlit/config.toml). Under the [features.mcp] section, you can configure SSE and stdio separately: 
[features]
# ... other feature flags

[features.mcp.sse]
    # Enable or disable the SSE connection type globally
    enabled = true

[features.mcp.stdio]
    # Enable or disable the stdio connection type globally
    enabled = true
    # Define an allowlist of executables for the stdio type.
    # Only the base names of executables listed here can be used.
    # This is a crucial security measure for stdio connections.
    # Example: allows running `npx ...` and `uvx ...` but blocks others.
    allowed_executables = [ "npx", "uvx" ]

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Setup

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1. Register Connection Handlers

To use MCP in your Chainlit application, you need to implement the on_mcp_connect handler. The on_mcp_disconnect handler is optional but recommended for proper cleanup. 
import chainlit as cl
from mcp import ClientSession

@cl.on_mcp_connect
async def on_mcp_connect(connection, session: ClientSession):
    """Called when an MCP connection is established"""
    # Your connection initialization code here
    # This handler is required for MCP to work
    
@cl.on_mcp_disconnect
async def on_mcp_disconnect(name: str, session: ClientSession):
    """Called when an MCP connection is terminated"""
    # Your cleanup code here
    # This handler is optional

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2. Client Configuration

The client needs to provide the connection details through the Chainlit interface. This includes:
  • Connection name (unique identifier)
  • Client type (sse or stdio)
  • For SSE: URL endpoint
  • For stdio: Full command (e.g., npx your-tool-package or uvx your-tool-package)

Adding an MCP

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Working with MCP Connections

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Retrieving Available Tools

Upon connection, you can discover the available tools provided by the MCP service: 
@cl.on_mcp_connect
async def on_mcp(connection, session: ClientSession):
    # List available tools
    result = await session.list_tools()
    
    # Process tool metadata
    tools = [{
        "name": t.name,
        "description": t.description,
        "input_schema": t.inputSchema,
    } for t in result.tools]
    
    # Store tools for later use
    mcp_tools = cl.user_session.get("mcp_tools", {})
    mcp_tools[connection.name] = tools
    cl.user_session.set("mcp_tools", mcp_tools)

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Executing Tools

You can execute tools using the MCP session: 
@cl.step(type="tool") 
async def call_tool(tool_use):
    tool_name = tool_use.name
    tool_input = tool_use.input
    
    # Find appropriate MCP connection for this tool
    mcp_name = find_mcp_for_tool(tool_name)
    
    # Get the MCP session
    mcp_session, _ = cl.context.session.mcp_sessions.get(mcp_name)
    
    # Call the tool
    result = await mcp_session.call_tool(tool_name, tool_input)
    
    return result

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Integrating with LLMs

MCP tools can be seamlessly integrated with LLMs that support tool calling: 
async def call_model_with_tools():
    # Get tools from all MCP connections
    mcp_tools = cl.user_session.get("mcp_tools", {})
    all_tools = [tool for connection_tools in mcp_tools.values() for tool in connection_tools]
    
    # Call your LLM with the tools
    response = await your_llm_client.call(
        messages=messages,
        tools=all_tools
    )
    
    # Handle tool calls if needed
    if response.has_tool_calls():
        # Process tool calls
        pass
        
    return response

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Session Management

MCP connections are managed at the session level. Each WebSocket session can have multiple named MCP connections. The connections are cleaned up when:
  1. The user explicitly disconnects
  2. The same connection name is reused (old connection is replaced)
  3. The WebSocket session ends