MCP Python SDK
Python implementation of the Model Context Protocol (MCP)
Table of Contents
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:
- Build MCP clients that can connect to any MCP server
- Create MCP servers that expose resources, prompts and tools
- Use standard transports like stdio, SSE, and Streamable HTTP
- Handle all MCP protocol messages and lifecycle events
Installation
Adding MCP to your python project
We recommend using uv to manage your Python projects.
If you haven't created a uv-managed project yet, create one:
uv init mcp-server-demo
cd mcp-server-demo
Then add MCP to your project dependencies:
Alternatively, for projects using pip for dependencies:
To run the mcp command with uv:
Quickstart
Let's create a simple MCP server that exposes a calculator tool and some data:
# server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("Demo")
# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
"""Get a personalized greeting"""
return f"Hello, {name}!"
You can install this server in Claude Desktop and interact with it right away by running:
Alternatively, you can test it with the MCP Inspector:
What is MCP?
The Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions. MCP servers can:
- Expose data through Resources (think of these sort of like GET endpoints; they are used to load information into the LLM's context)
- Provide functionality through Tools (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)
- Define interaction patterns through Prompts (reusable templates for LLM interactions)
- And more!
Core Concepts
Server
The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:
# Add lifespan support for startup/shutdown with strong typing
from contextlib import asynccontextmanager
from collections.abc import AsyncIterator
from dataclasses import dataclass
from fake_database import Database # Replace with your actual DB type
from mcp.server.fastmcp import FastMCP
# Create a named server
mcp = FastMCP("My App")
# Specify dependencies for deployment and development
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])
@dataclass
class AppContext:
db: Database
@asynccontextmanager
async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:
"""Manage application lifecycle with type-safe context"""
# Initialize on startup
db = await Database.connect()
try:
yield AppContext(db=db)
finally:
# Cleanup on shutdown
await db.disconnect()
# Pass lifespan to server
mcp = FastMCP("My App", lifespan=app_lifespan)
# Access type-safe lifespan context in tools
@mcp.tool()
def query_db() -> str:
"""Tool that uses initialized resources"""
ctx = mcp.get_context()
db = ctx.request_context.lifespan_context["db"]
return db.query()
Resources
Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
@mcp.resource("config://app", title="Application Configuration")
def get_config() -> str:
"""Static configuration data"""
return "App configuration here"
@mcp.resource("users://{user_id}/profile", title="User Profile")
def get_user_profile(user_id: str) -> str:
"""Dynamic user data"""
return f"Profile data for user {user_id}"
Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:
import httpx
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
@mcp.tool(title="BMI Calculator")
def calculate_bmi(weight_kg: float, height_m: float) -> float:
"""Calculate BMI given weight in kg and height in meters"""
return weight_kg / (height_m**2)
@mcp.tool(title="Weather Fetcher")
async def fetch_weather(city: str) -> str:
"""Fetch current weather for a city"""
async with httpx.AsyncClient() as client:
response = await client.get(f"https://api.weather.com/{city}")
return response.text
Structured Output
Tools will return structured results by default, if their return type
annotation is compatible. Otherwise, they will return unstructured results.
Structured output supports these return types:
- Pydantic models (BaseModel subclasses)
- TypedDicts
- Dataclasses and other classes with type hints
dict[str, T] (where T is any JSON-serializable type)- Primitive types (str, int, float, bool, bytes, None) - wrapped in
{"result": value} - Generic types (list, tuple, Union, Optional, etc.) - wrapped in
{"result": value}
Classes without type hints cannot be serialized for structured output. Only
classes with properly annotated attributes will be converted to Pydantic models
for schema generation and validation.
Structured results are automatically validated against the output schema
generated from the annotation. This ensures the tool returns well-typed,
validated data that clients can easily process.
Note: For backward compatibility, unstructured results are also
returned. Unstructured results are provided for backward compatibility
with previous versions of the MCP specification, and are quirks-compatible
with previous versions of FastMCP in the current version of the SDK.
Note: In cases where a tool function's return type annotation
causes the tool to be classified as structured and this is undesirable,
the classification can be suppressed by passing structured_output=False
to the @tool decorator.
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field
from typing import TypedDict
mcp = FastMCP("Weather Service")
# Using Pydantic models for rich structured data
class WeatherData(BaseModel):
temperature: float = Field(description="Temperature in Celsius")
humidity: float = Field(description="Humidity percentage")
condition: str
wind_speed: float
@mcp.tool()
def get_weather(city: str) -> WeatherData:
"""Get structured weather data"""
return WeatherData(
temperature=22.5, humidity=65.0, condition="partly cloudy", wind_speed=12.3
)
# Using TypedDict for simpler structures
class LocationInfo(TypedDict):
latitude: float
longitude: float
name: str
@mcp.tool()
def get_location(address: str) -> LocationInfo:
"""Get location coordinates"""
return LocationInfo(latitude=51.5074, longitude=-0.1278, name="London, UK")
# Using dict[str, Any] for flexible schemas
@mcp.tool()
def get_statistics(data_type: str) -> dict[str, float]:
"""Get various statistics"""
return {"mean": 42.5, "median": 40.0, "std_dev": 5.2}
# Ordinary classes with type hints work for structured output
class UserProfile:
name: str
age: int
email: str | None = None
def __init__(self, name: str, age: int, email: str | None = None):
self.name = name
self.age = age
self.email = email
@mcp.tool()
def get_user(user_id: str) -> UserProfile:
"""Get user profile - returns structured data"""
return UserProfile(name="Alice", age=30, email="alice@example.com")
# Classes WITHOUT type hints cannot be used for structured output
class UntypedConfig:
def __init__(self, setting1, setting2):
self.setting1 = setting1
self.setting2 = setting2
@mcp.tool()
def get_config() -> UntypedConfig:
"""This returns unstructured output - no schema generated"""
return UntypedConfig("value1", "value2")
# Lists and other types are wrapped automatically
@mcp.tool()
def list_cities() -> list[str]:
"""Get a list of cities"""
return ["London", "Paris", "Tokyo"]
# Returns: {"result": ["London", "Paris", "Tokyo"]}
@mcp.tool()
def get_temperature(city: str) -> float:
"""Get temperature as a simple float"""
return 22.5
# Returns: {"result": 22.5}
Prompts
Prompts are reusable templates that help LLMs interact with your server effectively:
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base
mcp = FastMCP("My App")
@mcp.prompt(title="Code Review")
def review_code(code: str) -> str:
return f"Please review this code:\n\n{code}"
@mcp.prompt(title="Debug Assistant")
def debug_error(error: str) -> list[base.Message]:
return [
base.UserMessage("I'm seeing this error:"),
base.UserMessage(error),
base.AssistantMessage("I'll help debug that. What have you tried so far?"),
]
Images
FastMCP provides an Image class that automatically handles image data:
from mcp.server.fastmcp import FastMCP, Image
from PIL import Image as PILImage
mcp = FastMCP("My App")
@mcp.tool()
def create_thumbnail(image_path: str) -> Image:
"""Create a thumbnail from an image"""
img = PILImage.open(image_path)
img.thumbnail((100, 100))
return Image(data=img.tobytes(), format="png")
Context
The Context object gives your tools and resources access to MCP capabilities:
from mcp.server.fastmcp import FastMCP, Context
mcp = FastMCP("My App")
@mcp.tool()
async def long_task(files: list[str], ctx: Context) -> str:
"""Process multiple files with progress tracking"""
for i, file in enumerate(files):
ctx.info(f"Processing {file}")
await ctx.report_progress(i, len(files))
data, mime_type = await ctx.read_resource(f"file://{file}")
return "Processing complete"
Completions
MCP supports providing completion suggestions for prompt arguments and resource template parameters. With the context parameter, servers can provide completions based on previously resolved values:
Client usage:
from mcp.client.session import ClientSession
from mcp.types import ResourceTemplateReference
async def use_completion(session: ClientSession):
# Complete without context
result = await session.complete(
ref=ResourceTemplateReference(
type="ref/resource", uri="github://repos/{owner}/{repo}"
),
argument={"name": "owner", "value": "model"},
)
# Complete with context - repo suggestions based on owner
result = await session.complete(
ref=ResourceTemplateReference(
type="ref/resource", uri="github://repos/{owner}/{repo}"
),
argument={"name": "repo", "value": "test"},
context_arguments={"owner": "modelcontextprotocol"},
)
Server implementation:
from mcp.server import Server
from mcp.types import (
Completion,
CompletionArgument,
CompletionContext,
PromptReference,
ResourceTemplateReference,
)
server = Server("example-server")
@server.completion()
async def handle_completion(
ref: PromptReference | ResourceTemplateReference,
argument: CompletionArgument,
context: CompletionContext | None,
) -> Completion | None:
if isinstance(ref, ResourceTemplateReference):
if ref.uri == "github://repos/{owner}/{repo}" and argument.name == "repo":
# Use context to provide owner-specific repos
if context and context.arguments:
owner = context.arguments.get("owner")
if owner == "modelcontextprotocol":
repos = ["python-sdk", "typescript-sdk", "specification"]
# Filter based on partial input
filtered = [r for r in repos if r.startswith(argument.value)]
return Completion(values=filtered)
return None
Elicitation
Request additional information from users during tool execution:
from mcp.server.fastmcp import FastMCP, Context
from mcp.server.elicitation import (
AcceptedElicitation,
DeclinedElicitation,
CancelledElicitation,
)
from pydantic import BaseModel, Field
mcp = FastMCP("Booking System")
@mcp.tool()
async def book_table(date: str, party_size: int, ctx: Context) -> str:
"""Book a table with confirmation"""
# Schema must only contain primitive types (str, int, float, bool)
class ConfirmBooking(BaseModel):
confirm: bool = Field(description="Confirm booking?")
notes: str = Field(default="", description="Special requests")
result = await ctx.elicit(
message=f"Confirm booking for {party_size} on {date}?", schema=ConfirmBooking
)
match result:
case AcceptedElicitation(data=data):
if data.confirm:
return f"Booked! Notes: {data.notes or 'None'}"
return "Booking cancelled"
case DeclinedElicitation():
return "Booking declined"
case CancelledElicitation():
return "Booking cancelled"
The elicit() method returns an ElicitationResult with:
action: "accept", "decline", or "cancel"data: The validated response (only when accepted)validation_error: Any validation error message
Authentication
Authentication can be used by servers that want to expose tools accessing protected resources.
mcp.server.auth implements OAuth 2.1 resource server functionality, where MCP servers act as Resource Servers (RS) that validate tokens issued by separate Authorization Servers (AS). This follows the MCP authorization specification and implements RFC 9728 (Protected Resource Metadata) for AS discovery.
MCP servers can use authentication by providing an implementation of the TokenVerifier protocol:
from mcp import FastMCP
from mcp.server.auth.provider import TokenVerifier, TokenInfo
from mcp.server.auth.settings import AuthSettings
class MyTokenVerifier(TokenVerifier):
# Implement token validation logic (typically via token introspection)
async def verify_token(self, token: str) -> TokenInfo:
# Verify with your authorization server
...
mcp = FastMCP(
"My App",
token_verifier=MyTokenVerifier(),
auth=AuthSettings(
issuer_url="https://auth.example.com",
resource_server_url="http://localhost:3001",
required_scopes=["mcp:read", "mcp:write"],
),
)
For a complete example with separate Authorization Server and Resource Server implementations, see examples/servers/simple-auth/.
Architecture:
- Authorization Server (AS): Handles OAuth flows, user authentication, and token issuance
- Resource Server (RS): Your MCP server that validates tokens and serves protected resources
- Client: Discovers AS through RFC 9728, obtains tokens, and uses them with the MCP server
See TokenVerifier for more details on implementing token validation.
Running Your Server
Development Mode
The fastest way to test and debug your server is with the MCP Inspector:
mcp dev server.py
# Add dependencies
mcp dev server.py --with pandas --with numpy
# Mount local code
mcp dev server.py --with-editable .
Claude Desktop Integration
Once your server is ready, install it in Claude Desktop:
mcp install server.py
# Custom name
mcp install server.py --name "My Analytics Server"
# Environment variables
mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
mcp install server.py -f .env
Direct Execution
For advanced scenarios like custom deployments:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
if __name__ == "__main__":
mcp.run()
Run it with:
python server.py
# or
mcp run server.py
Note that mcp run or mcp dev only supports server using FastMCP and not the low-level server variant.
Streamable HTTP Transport
Note: Streamable HTTP transport is superseding SSE transport for production deployments.
from mcp.server.fastmcp import FastMCP
# Stateful server (maintains session state)
mcp = FastMCP("StatefulServer")
# Stateless server (no session persistence)
mcp = FastMCP("StatelessServer", stateless_http=True)
# Stateless server (no session persistence, no sse stream with supported client)
mcp = FastMCP("StatelessServer", stateless_http=True, json_response=True)
# Run server with streamable_http transport
mcp.run(transport="streamable-http")
You can mount multiple FastMCP servers in a FastAPI application:
# echo.py
from mcp.server.fastmcp import FastMCP
mcp = FastMCP(name="EchoServer", stateless_http=True)
@mcp.tool(description="A simple echo tool")
def echo(message: str) -> str:
return f"Echo: {message}"
# math.py
from mcp.server.fastmcp import FastMCP
mcp = FastMCP(name="MathServer", stateless_http=True)
@mcp.tool(description="A simple add tool")
def add_two(n: int) -> int:
return n + 2
# main.py
import contextlib
from fastapi import FastAPI
from mcp.echo import echo
from mcp.math import math
# Create a combined lifespan to manage both session managers
@contextlib.asynccontextmanager
async def lifespan(app: FastAPI):
async with contextlib.AsyncExitStack() as stack:
await stack.enter_async_context(echo.mcp.session_manager.run())
await stack.enter_async_context(math.mcp.session_manager.run())
yield
app = FastAPI(lifespan=lifespan)
app.mount("/echo", echo.mcp.streamable_http_app())
app.mount("/math", math.mcp.streamable_http_app())
For low level server with Streamable HTTP implementations, see:
The streamable HTTP transport supports:
- Stateful and stateless operation modes
- Resumability with event stores
- JSON or SSE response formats
- Better scalability for multi-node deployments
Mounting to an Existing ASGI Server
Note: SSE transport is being superseded by Streamable HTTP transport.
By default, SSE servers are mounted at /sse and Streamable HTTP servers are mounted at /mcp. You can customize these paths using the methods described below.
You can mount the SSE server to an existing ASGI server using the sse_app method. This allows you to integrate the SSE server with other ASGI applications.
from starlette.applications import Starlette
from starlette.routing import Mount, Host
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
# Mount the SSE server to the existing ASGI server
app = Starlette(
routes=[
Mount('/', app=mcp.sse_app()),
]
)
# or dynamically mount as host
app.router.routes.append(Host('mcp.acme.corp', app=mcp.sse_app()))
When mounting multiple MCP servers under different paths, you can configure the mount path in several ways:
from starlette.applications import Starlette
from starlette.routing import Mount
from mcp.server.fastmcp import FastMCP
# Create multiple MCP servers
github_mcp = FastMCP("GitHub API")
browser_mcp = FastMCP("Browser")
curl_mcp = FastMCP("Curl")
search_mcp = FastMCP("Search")
# Method 1: Configure mount paths via settings (recommended for persistent configuration)
github_mcp.settings.mount_path = "/github"
browser_mcp.settings.mount_path = "/browser"
# Method 2: Pass mount path directly to sse_app (preferred for ad-hoc mounting)
# This approach doesn't modify the server's settings permanently
# Create Starlette app with multiple mounted servers
app = Starlette(
routes=[
# Using settings-based configuration
Mount("/github", app=github_mcp.sse_app()),
Mount("/browser", app=browser_mcp.sse_app()),
# Using direct mount path parameter
Mount("/curl", app=curl_mcp.sse_app("/curl")),
Mount("/search", app=search_mcp.sse_app("/search")),
]
)
# Method 3: For direct execution, you can also pass the mount path to run()
if __name__ == "__main__":
search_mcp.run(transport="sse", mount_path="/search")
For more information on mounting applications in Starlette, see the Starlette documentation.
Examples
Echo Server
A simple server demonstrating resources, tools, and prompts:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Echo")
@mcp.resource("echo://{message}")
def echo_resource(message: str) -> str:
"""Echo a message as a resource"""
return f"Resource echo: {message}"
@mcp.tool()
def echo_tool(message: str) -> str:
"""Echo a message as a tool"""
return f"Tool echo: {message}"
@mcp.prompt()
def echo_prompt(message: str) -> str:
"""Create an echo prompt"""
return f"Please process this message: {message}"
SQLite Explorer
A more complex example showing database integration:
import sqlite3
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("SQLite Explorer")
@mcp.resource("schema://main")
def get_schema() -> str:
"""Provide the database schema as a resource"""
conn = sqlite3.connect("database.db")
schema = conn.execute("SELECT sql FROM sqlite_master WHERE type='table'").fetchall()
return "\n".join(sql[0] for sql in schema if sql[0])
@mcp.tool()
def query_data(sql: str) -> str:
"""Execute SQL queries safely"""
conn = sqlite3.connect("database.db")
try:
result = conn.execute(sql).fetchall()
return "\n".join(str(row) for row in result)
except Exception as e:
return f"Error: {str(e)}"
Advanced Usage
Low-Level Server
For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API:
from contextlib import asynccontextmanager
from collections.abc import AsyncIterator
from fake_database import Database # Replace with your actual DB type
from mcp.server import Server
@asynccontextmanager
async def server_lifespan(server: Server) -> AsyncIterator[dict]:
"""Manage server startup and shutdown lifecycle."""
# Initialize resources on startup
db = await Database.connect()
try:
yield {"db": db}
finally:
# Clean up on shutdown
await db.disconnect()
# Pass lifespan to server
server = Server("example-server", lifespan=server_lifespan)
# Access lifespan context in handlers
@server.call_tool()
async def query_db(name: str, arguments: dict) -> list:
ctx = server.request_context
db = ctx.lifespan_context["db"]
return await db.query(arguments["query"])
The lifespan API provides:
- A way to initialize resources when the server starts and clean them up when it stops
- Access to initialized resources through the request context in handlers
- Type-safe context passing between lifespan and request handlers
import mcp.server.stdio
import mcp.types as types
from mcp.server.lowlevel import NotificationOptions, Server
from mcp.server.models import InitializationOptions
# Create a server instance
server = Server("example-server")
@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():
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())
Caution: The mcp run and mcp dev tool doesn't support low-level server.
Structured Output Support
The low-level server supports structured output for tools, allowing you to return both human-readable content and machine-readable structured data. Tools can define an outputSchema to validate their structured output:
from types import Any
import mcp.types as types
from mcp.server.lowlevel import Server
server = Server("example-server")
@server.list_tools()
async def list_tools() -> list[types.Tool]:
return [
types.Tool(
name="calculate",
description="Perform mathematical calculations",
inputSchema={
"type": "object",
"properties": {
"expression": {"type": "string", "description": "Math expression"}
},
"required": ["expression"],
},
outputSchema={
"type": "object",
"properties": {
"result": {"type": "number"},
"expression": {"type": "string"},
},
"required": ["result", "expression"],
},
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> dict[str, Any]:
if name == "calculate":
expression = arguments["expression"]
try:
result = eval(expression) # Use a safe math parser
structured = {"result": result, "expression": expression}
# low-level server will validate structured output against the tool's
# output schema, and automatically serialize it into a TextContent block
# for backwards compatibility with pre-2025-06-18 clients.
return structured
except Exception as e:
raise ValueError(f"Calculation error: {str(e)}")
Tools can return data in three ways:
- Content only: Return a list of content blocks (default behavior before spec revision 2025-06-18)
- Structured data only: Return a dictionary that will be serialized to JSON (Introduced in spec revision 2025-06-18)
- Both: Return a tuple of (content, structured_data) preferred option to use for backwards compatibility
When an outputSchema is defined, the server automatically validates the structured output against the schema. This ensures type safety and helps catch errors early.
Writing MCP Clients
The SDK provides a high-level client interface for connecting to MCP servers using various transports:
from mcp import ClientSession, StdioServerParameters, types
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
)
# Optional: create a sampling callback
async def handle_sampling_message(
message: types.CreateMessageRequestParams,
) -> types.CreateMessageResult:
return types.CreateMessageResult(
role="assistant",
content=types.TextContent(
type="text",
text="Hello, world! from model",
),
model="gpt-3.5-turbo",
stopReason="endTurn",
)
async def run():
async with stdio_client(server_params) as (read, write):
async with ClientSession(
read, write, sampling_callback=handle_sampling_message
) as session:
# Initialize the connection
await session.initialize()
# List available prompts
prompts = await session.list_prompts()
# Get a prompt
prompt = await session.get_prompt(
"example-prompt", arguments={"arg1": "value"}
)
# List available resources
resources = await session.list_resources()
# List available tools
tools = await session.list_tools()
# Read a resource
content, mime_type = 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())
Clients can also connect using Streamable HTTP transport:
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
async def main():
# Connect to a streamable HTTP server
async with streamablehttp_client("example/mcp") as (
read_stream,
write_stream,
_,
):
# Create a session using the client streams
async with ClientSession(read_stream, write_stream) as session:
# Initialize the connection
await session.initialize()
# Call a tool
tool_result = await session.call_tool("echo", {"message": "hello"})
Client Display Utilities
When building MCP clients, the SDK provides utilities to help display human-readable names for tools, resources, and prompts:
from mcp.shared.metadata_utils import get_display_name
from mcp.client.session import ClientSession
async def display_tools(session: ClientSession):
"""Display available tools with human-readable names"""
tools_response = await session.list_tools()
for tool in tools_response.tools:
# get_display_name() returns the title if available, otherwise the name
display_name = get_display_name(tool)
print(f"Tool: {display_name}")
if tool.description:
print(f" {tool.description}")
async def display_resources(session: ClientSession):
"""Display available resources with human-readable names"""
resources_response = await session.list_resources()
for resource in resources_response.resources:
display_name = get_display_name(resource)
print(f"Resource: {display_name} ({resource.uri})")
The get_display_name() function implements the proper precedence rules for displaying names:
- For tools:
title > annotations.title > name - For other objects:
title > name
This ensures your client UI shows the most user-friendly names that servers provide.
OAuth Authentication for Clients
The SDK includes authorization support for connecting to protected MCP servers:
from mcp.client.auth import OAuthClientProvider, TokenStorage
from mcp.client.session import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.shared.auth import OAuthClientInformationFull, OAuthClientMetadata, OAuthToken
class CustomTokenStorage(TokenStorage):
"""Simple in-memory token storage implementation."""
async def get_tokens(self) -> OAuthToken | None:
pass
async def set_tokens(self, tokens: OAuthToken) -> None:
pass
async def get_client_info(self) -> OAuthClientInformationFull | None:
pass
async def set_client_info(self, client_info: OAuthClientInformationFull) -> None:
pass
async def main():
# Set up OAuth authentication
oauth_auth = OAuthClientProvider(
server_url="https://api.example.com",
client_metadata=OAuthClientMetadata(
client_name="My Client",
redirect_uris=["http://localhost:3000/callback"],
grant_types=["authorization_code", "refresh_token"],
response_types=["code"],
),
storage=CustomTokenStorage(),
redirect_handler=lambda url: print(f"Visit: {url}"),
callback_handler=lambda: ("auth_code", None),
)
# Use with streamable HTTP client
async with streamablehttp_client(
"https://api.example.com/mcp", auth=oauth_auth
) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
# Authenticated session ready
For a complete working example, see examples/clients/simple-auth-client/.
MCP Primitives
The MCP protocol defines three core primitives that servers can implement:
| 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 |
Server Capabilities
MCP servers declare capabilities during initialization:
| Capability | Feature Flag | Description |
|---|
prompts | listChanged | Prompt template management |
resources | subscribelistChanged | Resource exposure and updates |
tools | listChanged | Tool discovery and execution |
logging | - | Server logging configuration |
completion | - | Argument completion suggestions |
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.