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MCP Python SDK

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Python implementation of the Model Context Protocol (MCP), providing both client and server capabilities for integrating with LLM surfaces.

Overview

The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:

Installation

We recommend the use of uv to manage your Python projects:

uv add mcp

Alternatively, add mcp to your requirements.txt:

pip install mcp
# or add to requirements.txt
pip install -r requirements.txt

Overview

MCP servers provide focused functionality like resources, tools, prompts, and other capabilities that can be reused across many client applications. These servers are designed to be easy to build, highly composable, and modular.

Key design principles

Server provided primitives

Client provided primitives

Connections between clients and servers are established through transports like stdio or SSE (Note that most clients support stdio, but not SSE at the moment). The transport layer handles message framing, delivery, and error handling.

Quick Start

Creating a Server

MCP servers follow a decorator approach to register handlers for MCP primitives like resources, prompts, and tools. The goal is to provide a simple interface for exposing capabilities to LLM clients.

example_server.py

# /// script
# dependencies = [
#   "mcp"
# ]
# ///
from mcp.server import Server, NotificationOptions
from mcp.server.models import InitializationOptions
import mcp.server.stdio
import mcp.types as types

# Create a server instance
server = Server("example-server")

# Add prompt capabilities
@server.list_prompts()
async def handle_list_prompts() -> list[types.Prompt]:
    return [
        types.Prompt(
            name="example-prompt",
            description="An example prompt template",
            arguments=[
                types.PromptArgument(
                    name="arg1",
                    description="Example argument",
                    required=True
                )
            ]
        )
    ]

@server.get_prompt()
async def handle_get_prompt(
    name: str,
    arguments: dict[str, str] | None
) -> types.GetPromptResult:
    if name != "example-prompt":
        raise ValueError(f"Unknown prompt: {name}")

    return types.GetPromptResult(
        description="Example prompt",
        messages=[
            types.PromptMessage(
                role="user",
                content=types.TextContent(
                    type="text",
                    text="Example prompt text"
                )
            )
        ]
    )

async def run():
    # Run the server as STDIO
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="example",
                server_version="0.1.0",
                capabilities=server.get_capabilities(
                    notification_options=NotificationOptions(),
                    experimental_capabilities={},
                )
            )
        )

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

Creating a Client

example_client.py

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="python", # Executable
    args=["example_server.py"], # Optional command line arguments
    env=None # Optional environment variables
)

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

            # The example server only supports prompt primitives:
        
            # List available prompts
            prompts = await session.list_prompts()

            # Get a prompt
            prompt = await session.get_prompt("example-prompt", arguments={"arg1": "value"})

            """
            Other example calls include:

            # List available resources
            resources = await session.list_resources()

            # List available tools
            tools = await session.list_tools()

            # Read a resource
            resource = await session.read_resource("file://some/path")

            # Call a tool
            result = await session.call_tool("tool-name", arguments={"arg1": "value"})
            """

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

Primitives

The MCP Python SDK provides decorators that map to the core protocol primitives. Each primitive follows a different interaction pattern based on how it is controlled and used:

PrimitiveControlDescriptionExample Use
PromptsUser-controlledInteractive templates invoked by user choiceSlash commands, menu options
ResourcesApplication-controlledContextual data managed by the client applicationFile contents, API responses
ToolsModel-controlledFunctions exposed to the LLM to take actionsAPI calls, data updates

User-Controlled Primitives

Prompts are designed to be explicitly selected by users for their interactions with LLMs.

DecoratorDescription
@server.list_prompts()List available prompt templates
@server.get_prompt()Get a specific prompt with arguments

Application-Controlled Primitives

Resources are controlled by the client application, which decides how and when they should be used based on its own logic.

DecoratorDescription
@server.list_resources()List available resources
@server.read_resource()Read a specific resource's content
@server.subscribe_resource()Subscribe to resource updates

Model-Controlled Primitives

Tools are exposed to LLMs to enable automated actions, with user approval.

DecoratorDescription
@server.list_tools()List available tools
@server.call_tool()Execute a tool with arguments

Server Management

Additional decorators for server functionality:

DecoratorDescription
@server.set_logging_level()Update server logging level

Capabilities

MCP servers declare capabilities during initialization. These map to specific decorators:

CapabilityFeature FlagDecoratorsDescription
promptslistChanged@list_prompts<br/>@get_promptPrompt template management
resourcessubscribe<br/>listChanged@list_resources<br/>@read_resource<br/>@subscribe_resourceResource exposure and updates
toolslistChanged@list_tools<br/>@call_toolTool discovery and execution
logging-@set_logging_levelServer logging configuration
completion-@complete_argumentArgument completion suggestions

Capabilities are negotiated during connection initialization. Servers only need to implement the decorators for capabilities they support.

Client Interaction

The MCP Python SDK enables servers to interact with clients through request context and session management. This allows servers to perform operations like LLM sampling and progress tracking.

Request Context

The Request Context provides access to the current request and client session. It can be accessed through server.request_context and enables:

Example using request context for LLM sampling:

@server.call_tool()
async def handle_call_tool(name: str, arguments: dict) -> list[types.TextContent]:
    # Access the current request context
    context = server.request_context

    # Use the session to sample from the client's LLM
    result = await context.session.create_message(
        messages=[
            types.SamplingMessage(
                role="user",
                content=types.TextContent(
                    type="text",
                    text="Analyze this data: " + json.dumps(arguments)
                )
            )
        ],
        max_tokens=100
    )

    return [types.TextContent(type="text", text=result.content.text)]

Using request context for progress updates:

@server.call_tool()
async def handle_call_tool(name: str, arguments: dict) -> list[types.TextContent]:
    context = server.request_context

    if progress_token := context.meta.progressToken:
        # Send progress notifications
        await context.session.send_progress_notification(
            progress_token=progress_token,
            progress=0.5,
            total=1.0
        )

    # Perform operation...

    if progress_token:
        await context.session.send_progress_notification(
            progress_token=progress_token,
            progress=1.0,
            total=1.0
        )

    return [types.TextContent(type="text", text="Operation complete")]

The request context is automatically set for each request and provides a safe way to access the current client session and request metadata.

Documentation

Contributing

We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the contributing guide to get started.

License

This project is licensed under the MIT License - see the LICENSE file for details.