Python implementation of the Model Context Protocol (MCP), providing both client and server capabilities for integrating with LLM surfaces.
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:
- Build MCP clients that can connect to any MCP server
- Create MCP servers that expose resources, prompts and tools
- Use standard transports like stdio and SSE
- Handle all MCP protocol messages and lifecycle events
We recommend the use of uv to manage your Python projects:
uv add mcpAlternatively, add mcp to your requirements.txt:
pip install mcp
# or add to requirements.txt
pip install -r requirements.txt
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.
- Servers are extremely easy to build with clear, simple interfaces
- Multiple servers can be composed seamlessly through a shared protocol
- Each server operates in isolation and cannot access conversation context
- Features can be added progressively through capability negotiation
- Prompts: Templatable text
- Resources: File-like attachments
- Tools: Functions that models can call
- Utilities:
- Completion: Auto-completion provider for prompt arguments or resource URI templates
- Logging: Logging to the client
- Pagination*: Pagination for long results
- Sampling: Allow servers to sample using client models
- Roots: Information about locations to operate on (e.g., directories)
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.
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(
8000
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())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())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:
| Primitive | Control | Description | Example Use |
|---|---|---|---|
| Prompts | User-controlled | Interactive templates invoked by user choice | Slash commands, menu options |
| Resources | Application-controlled | Contextual data managed by the client application | File contents, API responses |
| Tools | Model-controlled | Functions exposed to the LLM to take actions | API calls, data updates |
Prompts are designed to be explicitly selected by users for their interactions with LLMs.
| Decorator | Description |
|---|---|
@server.list_prompts() |
List available prompt templates |
@server.get_prompt() |
Get a specific prompt with arguments |
Resources are controlled by the client application, which decides how and when they should be used based on its own logic.
| Decorator | Description |
|---|---|
@server.list_resources() |
List available resources |
@server.read_resource() |
Read a specific resource's content |
@server.subscribe_resource() |
Subscribe to resource updates |
Tools are exposed to LLMs to enable automated actions, with user approval.
| Decorator | Description |
|---|---|
@server.list_tools() |
List available tools |
@server.call_tool() |
Execute a tool with arguments |
Additional decorators for server functionality:
| Decorator | Description |
|---|---|
@server.set_logging_level() |
Update server logging level |
MCP servers declare capabilities during initialization. These map to specific decorators:
| Capability | Feature Flag | Decorators | Description |
|---|---|---|---|
prompts |
listChanged |
@list_prompts@get_prompt |
Prompt template management |
resources |
subscribelistChanged |
@list_resources@read_resource@subscribe_resource |
Resource exposure and updates |
tools |
listChanged |
@list_tools@call_tool |
Tool discovery and execution |
logging |
- | @set_logging_level |
Server logging configuration |
completion |
- | @complete_argument |
Argument completion suggestions |
Capabilities are negotiated during connection initialization. Servers only need to implement the decorators for capabilities they support.
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.
The Request Context provides access to the current request and client session. It can be accessed through server.request_context and enables:
- Sampling from the client's LLM
- Sending progress updates
- Logging messages
- Accessing request metadata
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.
- Model Context Protocol documentation
- Model Context Protocol specification
- Officially supported servers
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.
This project is licensed under the MIT License - see the LICENSE file for details.