MCP Python SDK

Python implementation of the Model Context Protocol (MCP)

Table of Contents

MCP Python SDK

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:

uv add "mcp[cli]"

Alternatively, for projects using pip for dependencies:

pip install "mcp[cli]"

Running the standalone MCP development tools

To run the mcp command with uv:

uv run mcp

Quickstart

Let's create a simple MCP server that exposes a calculator tool and some data:

"""
FastMCP quickstart example.

cd to the `examples/snippets/clients` directory and run:
    uv run server fastmcp_quickstart stdio
"""

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}!"


# Add a prompt
@mcp.prompt()
def greet_user(name: str, style: str = "friendly") -> str:
    """Generate a greeting prompt"""
    styles = {
        "friendly": "Please write a warm, friendly greeting",
        "formal": "Please write a formal, professional greeting",
        "casual": "Please write a casual, relaxed greeting",
    }

    return f"{styles.get(style, styles['friendly'])} for someone named {name}."

Full example: examples/snippets/servers/fastmcp_quickstart.py

You can install this server in Claude Desktop and interact with it right away by running:

uv run mcp install server.py

Alternatively, you can test it with the MCP Inspector:

uv run mcp dev server.py

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:

"""Example showing lifespan support for startup/shutdown with strong typing."""

from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from dataclasses import dataclass

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession


# Mock database class for example
class Database:
    """Mock database class for example."""

    @classmethod
    async def connect(cls) -> "Database":
        """Connect to database."""
        return cls()

    async def disconnect(self) -> None:
        """Disconnect from database."""
        pass

    def query(self) -> str:
        """Execute a query."""
        return "Query result"


@dataclass
class AppContext:
    """Application context with typed dependencies."""

    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(ctx: Context[ServerSession, AppContext]) -> str:
    """Tool that uses initialized resources."""
    db = ctx.request_context.lifespan_context.db
    return db.query()

Full example: examples/snippets/servers/lifespan_example.py

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(name="Resource Example")


@mcp.resource("file://documents/{name}")
def read_document(name: str) -> str:
    """Read a document by name."""
    # This would normally read from disk
    return f"Content of {name}"


@mcp.resource("config://settings")
def get_settings() -> str:
    """Get application settings."""
    return """{
  "theme": "dark",
  "language": "en",
  "debug": false
}"""

Full example: examples/snippets/servers/basic_resource.py

Tools

Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP(name="Tool Example")


@mcp.tool()
def sum(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b


@mcp.tool()
def get_weather(city: str, unit: str = "celsius") -> str:
    """Get weather for a city."""
    # This would normally call a weather API
    return f"Weather in {city}: 22degrees{unit[0].upper()}"

Full example: examples/snippets/servers/basic_tool.py

Tools can optionally receive a Context object by including a parameter with the Context type annotation. This context is automatically injected by the FastMCP framework and provides access to MCP capabilities:

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession

mcp = FastMCP(name="Progress Example")


@mcp.tool()
async def long_running_task(task_name: str, ctx: Context[ServerSession, None], steps: int = 5) -> str:
    """Execute a task with progress updates."""
    await ctx.info(f"Starting: {task_name}")

    for i in range(steps):
        progress = (i + 1) / steps
        await ctx.report_progress(
            progress=progress,
            total=1.0,
            message=f"Step {i + 1}/{steps}",
        )
        await ctx.debug(f"Completed step {i + 1}")

    return f"Task '{task_name}' completed"

Full example: examples/snippets/servers/tool_progress.py

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.

"""Example showing structured output with tools."""

from typing import TypedDict

from pydantic import BaseModel, Field

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Structured Output Example")


# Using Pydantic models for rich structured data
class WeatherData(BaseModel):
    """Weather information structure."""

    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 weather for a city - returns structured data."""
    # Simulated weather data
    return WeatherData(
        temperature=22.5,
        humidity=45.0,
        condition="sunny",
        wind_speed=5.2,
    )


# 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):  # type: ignore[reportMissingParameterType]
        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}

Full example: examples/snippets/servers/structured_output.py

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(name="Prompt Example")


@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?"),
    ]

Full example: examples/snippets/servers/basic_prompt.py

Images

FastMCP provides an Image class that automatically handles image data:

"""Example showing image handling with FastMCP."""

from PIL import Image as PILImage

from mcp.server.fastmcp import FastMCP, Image

mcp = FastMCP("Image Example")


@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")

Full example: examples/snippets/servers/images.py

Context

The Context object is automatically injected into tool and resource functions that request it via type hints. It provides access to MCP capabilities like logging, progress reporting, resource reading, user interaction, and request metadata.

Getting Context in Functions

To use context in a tool or resource function, add a parameter with the Context type annotation:

from mcp.server.fastmcp import Context, FastMCP

mcp = FastMCP(name="Context Example")


@mcp.tool()
async def my_tool(x: int, ctx: Context) -> str:
    """Tool that uses context capabilities."""
    # The context parameter can have any name as long as it's type-annotated
    return await process_with_context(x, ctx)

Context Properties and Methods

The Context object provides the following capabilities:

ctx.request_id - Unique ID for the current request
ctx.client_id - Client ID if available
ctx.fastmcp - Access to the FastMCP server instance (see FastMCP Properties)
ctx.session - Access to the underlying session for advanced communication (see Session Properties and Methods)
ctx.request_context - Access to request-specific data and lifespan resources (see Request Context Properties)
await ctx.debug(message) - Send debug log message
await ctx.info(message) - Send info log message
await ctx.warning(message) - Send warning log message
await ctx.error(message) - Send error log message
await ctx.log(level, message, logger_name=None) - Send log with custom level
await ctx.report_progress(progress, total=None, message=None) - Report operation progress
await ctx.read_resource(uri) - Read a resource by URI
await ctx.elicit(message, schema) - Request additional information from user with validation

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession

mcp = FastMCP(name="Progress Example")


@mcp.tool()
async def long_running_task(task_name: str, ctx: Context[ServerSession, None], steps: int = 5) -> str:
    """Execute a task with progress updates."""
    await ctx.info(f"Starting: {task_name}")

    for i in range(steps):
        progress = (i + 1) / steps
        await ctx.report_progress(
            progress=progress,
            total=1.0,
            message=f"Step {i + 1}/{steps}",
        )
        await ctx.debug(f"Completed step {i + 1}")

    return f"Task '{task_name}' completed"

Full example: examples/snippets/servers/tool_progress.py

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:

"""
cd to the `examples/snippets` directory and run:
    uv run completion-client
"""

import asyncio
import os

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from mcp.types import PromptReference, ResourceTemplateReference

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="uv",  # Using uv to run the server
    args=["run", "server", "completion", "stdio"],  # Server with completion support
    env={"UV_INDEX": os.environ.get("UV_INDEX", "")},
)


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

            # List available resource templates
            templates = await session.list_resource_templates()
            print("Available resource templates:")
            for template in templates.resourceTemplates:
                print(f"  - {template.uriTemplate}")

            # List available prompts
            prompts = await session.list_prompts()
            print("\nAvailable prompts:")
            for prompt in prompts.prompts:
                print(f"  - {prompt.name}")

            # Complete resource template arguments
            if templates.resourceTemplates:
                template = templates.resourceTemplates[0]
                print(f"\nCompleting arguments for resource template: {template.uriTemplate}")

                # Complete without context
                result = await session.complete(
                    ref=ResourceTemplateReference(type="ref/resource", uri=template.uriTemplate),
                    argument={"name": "owner", "value": "model"},
                )
                print(f"Completions for 'owner' starting with 'model': {result.completion.values}")

                # Complete with context - repo suggestions based on owner
                result = await session.complete(
                    ref=ResourceTemplateReference(type="ref/resource", uri=template.uriTemplate),
                    argument={"name": "repo", "value": ""},
                    context_arguments={"owner": "modelcontextprotocol"},
                )
                print(f"Completions for 'repo' with owner='modelcontextprotocol': {result.completion.values}")

            # Complete prompt arguments
            if prompts.prompts:
                prompt_name = prompts.prompts[0].name
                print(f"\nCompleting arguments for prompt: {prompt_name}")

                result = await session.complete(
                    ref=PromptReference(type="ref/prompt", name=prompt_name),
                    argument={"name": "style", "value": ""},
                )
                print(f"Completions for 'style' argument: {result.completion.values}")


def main():
    """Entry point for the completion client."""
    asyncio.run(run())


if __name__ == "__main__":
    main()

Full example: examples/snippets/clients/completion_client.py

Elicitation

Request additional information from users. This example shows an Elicitation during a Tool Call:

from pydantic import BaseModel, Field

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession

mcp = FastMCP(name="Elicitation Example")


class BookingPreferences(BaseModel):
    """Schema for collecting user preferences."""

    checkAlternative: bool = Field(description="Would you like to check another date?")
    alternativeDate: str = Field(
        default="2024-12-26",
        description="Alternative date (YYYY-MM-DD)",
    )


@mcp.tool()
async def book_table(date: str, time: str, party_size: int, ctx: Context[ServerSession, None]) -> str:
    """Book a table with date availability check."""
    # Check if date is available
    if date == "2024-12-25":
        # Date unavailable - ask user for alternative
        result = await ctx.elicit(
            message=(f"No tables available for {party_size} on {date}. Would you like to try another date?"),
            schema=BookingPreferences,
        )

        if result.action == "accept" and result.data:
            if result.data.checkAlternative:
                return f"[SUCCESS] Booked for {result.data.alternativeDate}"
            return "[CANCELLED] No booking made"
        return "[CANCELLED] Booking cancelled"

    # Date available
    return f"[SUCCESS] Booked for {date} at {time}"

Full example: examples/snippets/servers/elicitation.py

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

Sampling

Tools can interact with LLMs through sampling (generating text):

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession
from mcp.types import SamplingMessage, TextContent

mcp = FastMCP(name="Sampling Example")


@mcp.tool()
async def generate_poem(topic: str, ctx: Context[ServerSession, None]) -> str:
    """Generate a poem using LLM sampling."""
    prompt = f"Write a short poem about {topic}"

    result = await ctx.session.create_message(
        messages=[
            SamplingMessage(
                role="user",
                content=TextContent(type="text", text=prompt),
            )
        ],
        max_tokens=100,
    )

    if result.content.type == "text":
        return result.content.text
    return str(result.content)

Full example: examples/snippets/servers/sampling.py

Logging and Notifications

Tools can send logs and notifications through the context:

from mcp.server.fastmcp import Context, FastMCP
from mcp.server.session import ServerSession

mcp = FastMCP(name="Notifications Example")


@mcp.tool()
async def process_data(data: str, ctx: Context[ServerSession, None]) -> str:
    """Process data with logging."""
    # Different log levels
    await ctx.debug(f"Debug: Processing '{data}'")
    await ctx.info("Info: Starting processing")
    await ctx.warning("Warning: This is experimental")
    await ctx.error("Error: (This is just a demo)")

    # Notify about resource changes
    await ctx.session.send_resource_list_changed()

    return f"Processed: {data}"

Full example: examples/snippets/servers/notifications.py

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:

"""
Run from the repository root:
    uv run examples/snippets/servers/oauth_server.py
"""

from pydantic import AnyHttpUrl

from mcp.server.auth.provider import AccessToken, TokenVerifier
from mcp.server.auth.settings import AuthSettings
from mcp.server.fastmcp import FastMCP


class SimpleTokenVerifier(TokenVerifier):
    """Simple token verifier for demonstration."""

    async def verify_token(self, token: str) -> AccessToken | None:
        pass  # This is where you would implement actual token validation


# Create FastMCP instance as a Resource Server
mcp = FastMCP(
    "Weather Service",
    # Token verifier for authentication
    token_verifier=SimpleTokenVerifier(),
    # Auth settings for RFC 9728 Protected Resource Metadata
    auth=AuthSettings(
        issuer_url=AnyHttpUrl("https://auth.example.com"),  # Authorization Server URL
        resource_server_url=AnyHttpUrl("http://localhost:3001"),  # This server's URL
        required_scopes=["user"],
    ),
)


@mcp.tool()
async def get_weather(city: str = "London") -> dict[str, str]:
    """Get weather data for a city"""
    return {
        "city": city,
        "temperature": "22",
        "condition": "Partly cloudy",
        "humidity": "65%",
    }


if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Full example: examples/snippets/servers/oauth_server.py

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.

FastMCP Properties

The FastMCP server instance accessible via ctx.fastmcp provides access to server configuration and metadata:

ctx.fastmcp.name - The server's name as defined during initialization
ctx.fastmcp.instructions - Server instructions/description provided to clients
ctx.fastmcp.settings - Complete server configuration object containing:
- debug - Debug mode flag
- log_level - Current logging level
- host and port - Server network configuration
- mount_path, sse_path, streamable_http_path - Transport paths
- stateless_http - Whether the server operates in stateless mode
- And other configuration options

@mcp.tool()
def server_info(ctx: Context) -> dict:
    """Get information about the current server."""
    return {
        "name": ctx.fastmcp.name,
        "instructions": ctx.fastmcp.instructions,
        "debug_mode": ctx.fastmcp.settings.debug,
        "log_level": ctx.fastmcp.settings.log_level,
        "host": ctx.fastmcp.settings.host,
        "port": ctx.fastmcp.settings.port,
    }

Session Properties and Methods

The session object accessible via ctx.session provides advanced control over client communication:

ctx.session.client_params - Client initialization parameters and declared capabilities
await ctx.session.send_log_message(level, data, logger) - Send log messages with full control
await ctx.session.create_message(messages, max_tokens) - Request LLM sampling/completion
await ctx.session.send_progress_notification(token, progress, total, message) - Direct progress updates
await ctx.session.send_resource_updated(uri) - Notify clients that a specific resource changed
await ctx.session.send_resource_list_changed() - Notify clients that the resource list changed
await ctx.session.send_tool_list_changed() - Notify clients that the tool list changed
await ctx.session.send_prompt_list_changed() - Notify clients that the prompt list changed

@mcp.tool()
async def notify_data_update(resource_uri: str, ctx: Context) -> str:
    """Update data and notify clients of the change."""
    # Perform data update logic here
    
    # Notify clients that this specific resource changed
    await ctx.session.send_resource_updated(AnyUrl(resource_uri))
    
    # If this affects the overall resource list, notify about that too
    await ctx.session.send_resource_list_changed()
    
    return f"Updated {resource_uri} and notified clients"

Request Context Properties

The request context accessible via ctx.request_context contains request-specific information and resources:

ctx.request_context.lifespan_context - Access to resources initialized during server startup
- Database connections, configuration objects, shared services
- Type-safe access to resources defined in your server's lifespan function
ctx.request_context.meta - Request metadata from the client including:
- progressToken - Token for progress notifications
- Other client-provided metadata
ctx.request_context.request - The original MCP request object for advanced processing
ctx.request_context.request_id - Unique identifier for this request

# Example with typed lifespan context
@dataclass
class AppContext:
    db: Database
    config: AppConfig

@mcp.tool()
def query_with_config(query: str, ctx: Context) -> str:
    """Execute a query using shared database and configuration."""
    # Access typed lifespan context
    app_ctx: AppContext = ctx.request_context.lifespan_context
    
    # Use shared resources
    connection = app_ctx.db
    settings = app_ctx.config
    
    # Execute query with configuration
    result = connection.execute(query, timeout=settings.query_timeout)
    return str(result)

Full lifespan example: examples/snippets/servers/lifespan_example.py

Running Your Server

Development Mode

The fastest way to test and debug your server is with the MCP Inspector:

uv run mcp dev server.py

# Add dependencies
uv run mcp dev server.py --with pandas --with numpy

# Mount local code
uv run mcp dev server.py --with-editable .

Claude Desktop Integration

Once your server is ready, install it in Claude Desktop:

uv run mcp install server.py

# Custom name
uv run mcp install server.py --name "My Analytics Server"

# Environment variables
uv run mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
uv run mcp install server.py -f .env

Direct Execution

For advanced scenarios like custom deployments:

"""Example showing direct execution of an MCP server.

This is the simplest way to run an MCP server directly.
cd to the `examples/snippets` directory and run:
    uv run direct-execution-server
    or
    python servers/direct_execution.py
"""

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")


@mcp.tool()
def hello(name: str = "World") -> str:
    """Say hello to someone."""
    return f"Hello, {name}!"


def main():
    """Entry point for the direct execution server."""
    mcp.run()


if __name__ == "__main__":
    main()

Full example: examples/snippets/servers/direct_execution.py

Run it with:

python servers/direct_execution.py
# or
uv run mcp run servers/direct_execution.py

Note that uv run mcp run or uv run 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.

"""
Run from the repository root:
    uv run examples/snippets/servers/streamable_config.py
"""

from mcp.server.fastmcp import FastMCP

# Stateful server (maintains session state)
mcp = FastMCP("StatefulServer")

# Other configuration options:
# 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)


# Add a simple tool to demonstrate the server
@mcp.tool()
def greet(name: str = "World") -> str:
    """Greet someone by name."""
    return f"Hello, {name}!"


# Run server with streamable_http transport
if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Full example: examples/snippets/servers/streamable_config.py

You can mount multiple FastMCP servers in a Starlette application:

"""
Run from the repository root:
    uvicorn examples.snippets.servers.streamable_starlette_mount:app --reload
"""

import contextlib

from starlette.applications import Starlette
from starlette.routing import Mount

from mcp.server.fastmcp import FastMCP

# Create the Echo server
echo_mcp = FastMCP(name="EchoServer", stateless_http=True)


@echo_mcp.tool()
def echo(message: str) -> str:
    """A simple echo tool"""
    return f"Echo: {message}"


# Create the Math server
math_mcp = FastMCP(name="MathServer", stateless_http=True)


@math_mcp.tool()
def add_two(n: int) -> int:
    """Tool to add two to the input"""
    return n + 2


# Create a combined lifespan to manage both session managers
@contextlib.asynccontextmanager
async def lifespan(app: Starlette):
    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


# Create the Starlette app and mount the MCP servers
app = Starlette(
    routes=[
        Mount("/echo", echo_mcp.streamable_http_app()),
        Mount("/math", math_mcp.streamable_http_app()),
    ],
    lifespan=lifespan,
)

# Note: Clients connect to http://localhost:8000/echo/mcp and http://localhost:8000/math/mcp
# To mount at the root of each path (e.g., /echo instead of /echo/mcp):
# echo_mcp.settings.streamable_http_path = "/"
# math_mcp.settings.streamable_http_path = "/"

Full example: examples/snippets/servers/streamable_starlette_mount.py

For low level server with Streamable HTTP implementations, see:

Stateful server: examples/servers/simple-streamablehttp/
Stateless server: examples/servers/simple-streamablehttp-stateless/

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

CORS Configuration for Browser-Based Clients

If you'd like your server to be accessible by browser-based MCP clients, you'll need to configure CORS headers. The Mcp-Session-Id header must be exposed for browser clients to access it:

from starlette.applications import Starlette
from starlette.middleware.cors import CORSMiddleware

# Create your Starlette app first
starlette_app = Starlette(routes=[...])

# Then wrap it with CORS middleware
starlette_app = CORSMiddleware(
    starlette_app,
    allow_origins=["*"],  # Configure appropriately for production
    allow_methods=["GET", "POST", "DELETE"],  # MCP streamable HTTP methods
    expose_headers=["Mcp-Session-Id"],
)

This configuration is necessary because:

The MCP streamable HTTP transport uses the Mcp-Session-Id header for session management
Browsers restrict access to response headers unless explicitly exposed via CORS
Without this configuration, browser-based clients won't be able to read the session ID from initialization responses

Mounting to an Existing ASGI Server

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.

For more information on mounting applications in Starlette, see the Starlette documentation.

StreamableHTTP servers

You can mount the StreamableHTTP server to an existing ASGI server using the streamable_http_app method. This allows you to integrate the StreamableHTTP server with other ASGI applications.

Basic mounting

"""
Basic example showing how to mount StreamableHTTP server in Starlette.

Run from the repository root:
    uvicorn examples.snippets.servers.streamable_http_basic_mounting:app --reload
"""

from starlette.applications import Starlette
from starlette.routing import Mount

from mcp.server.fastmcp import FastMCP

# Create MCP server
mcp = FastMCP("My App")


@mcp.tool()
def hello() -> str:
    """A simple hello tool"""
    return "Hello from MCP!"


# Mount the StreamableHTTP server to the existing ASGI server
app = Starlette(
    routes=[
        Mount("/", app=mcp.streamable_http_app()),
    ]
)

Full example: examples/snippets/servers/streamable_http_basic_mounting.py

Host-based routing

"""
Example showing how to mount StreamableHTTP server using Host-based routing.

Run from the repository root:
    uvicorn examples.snippets.servers.streamable_http_host_mounting:app --reload
"""

from starlette.applications import Starlette
from starlette.routing import Host

from mcp.server.fastmcp import FastMCP

# Create MCP server
mcp = FastMCP("MCP Host App")


@mcp.tool()
def domain_info() -> str:
    """Get domain-specific information"""
    return "This is served from mcp.acme.corp"


# Mount using Host-based routing
app = Starlette(
    routes=[
        Host("mcp.acme.corp", app=mcp.streamable_http_app()),
    ]
)

Full example: examples/snippets/servers/streamable_http_host_mounting.py

Multiple servers with path configuration

"""
Example showing how to mount multiple StreamableHTTP servers with path configuration.

Run from the repository root:
    uvicorn examples.snippets.servers.streamable_http_multiple_servers:app --reload
"""

from starlette.applications import Starlette
from starlette.routing import Mount

from mcp.server.fastmcp import FastMCP

# Create multiple MCP servers
api_mcp = FastMCP("API Server")
chat_mcp = FastMCP("Chat Server")


@api_mcp.tool()
def api_status() -> str:
    """Get API status"""
    return "API is running"


@chat_mcp.tool()
def send_message(message: str) -> str:
    """Send a chat message"""
    return f"Message sent: {message}"


# Configure servers to mount at the root of each path
# This means endpoints will be at /api and /chat instead of /api/mcp and /chat/mcp
api_mcp.settings.streamable_http_path = "/"
chat_mcp.settings.streamable_http_path = "/"

# Mount the servers
app = Starlette(
    routes=[
        Mount("/api", app=api_mcp.streamable_http_app()),
        Mount("/chat", app=chat_mcp.streamable_http_app()),
    ]
)

Full example: examples/snippets/servers/streamable_http_multiple_servers.py

Path configuration at initialization

"""
Example showing path configuration during FastMCP initialization.

Run from the repository root:
    uvicorn examples.snippets.servers.streamable_http_path_config:app --reload
"""

from starlette.applications import Starlette
from starlette.routing import Mount

from mcp.server.fastmcp import FastMCP

# Configure streamable_http_path during initialization
# This server will mount at the root of wherever it's mounted
mcp_at_root = FastMCP("My Server", streamable_http_path="/")


@mcp_at_root.tool()
def process_data(data: str) -> str:
    """Process some data"""
    return f"Processed: {data}"


# Mount at /process - endpoints will be at /process instead of /process/mcp
app = Starlette(
    routes=[
        Mount("/process", app=mcp_at_root.streamable_http_app()),
    ]
)

Full example: examples/snippets/servers/streamable_http_path_config.py

SSE servers

Note: SSE transport is being superseded by Streamable HTTP transport.

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.

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:

"""
Run from the repository root:
    uv run examples/snippets/servers/lowlevel/lifespan.py
"""

from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from typing import Any

import mcp.server.stdio
import mcp.types as types
from mcp.server.lowlevel import NotificationOptions, Server
from mcp.server.models import InitializationOptions


# Mock database class for example
class Database:
    """Mock database class for example."""

    @classmethod
    async def connect(cls) -> "Database":
        """Connect to database."""
        print("Database connected")
        return cls()

    async def disconnect(self) -> None:
        """Disconnect from database."""
        print("Database disconnected")

    async def query(self, query_str: str) -> list[dict[str, str]]:
        """Execute a query."""
        # Simulate database query
        return [{"id": "1", "name": "Example", "query": query_str}]


@asynccontextmanager
async def server_lifespan(_server: Server) -> AsyncIterator[dict[str, Any]]:
    """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)


@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
    """List available tools."""
    return [
        types.Tool(
            name="query_db",
            description="Query the database",
            inputSchema={
                "type": "object",
                "properties": {"query": {"type": "string", "description": "SQL query to execute"}},
                "required": ["query"],
            },
        )
    ]


@server.call_tool()
async def query_db(name: str, arguments: dict[str, Any]) -> list[types.TextContent]:
    """Handle database query tool call."""
    if name != "query_db":
        raise ValueError(f"Unknown tool: {name}")

    # Access lifespan context
    ctx = server.request_context
    db = ctx.lifespan_context["db"]

    # Execute query
    results = await db.query(arguments["query"])

    return [types.TextContent(type="text", text=f"Query results: {results}")]


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


if __name__ == "__main__":
    import asyncio

    asyncio.run(run())

Full example: examples/snippets/servers/lowlevel/lifespan.py

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

"""
Run from the repository root:
uv run examples/snippets/servers/lowlevel/basic.py
"""

import asyncio

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]:
    """List available prompts."""
    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:
    """Get a specific prompt by name."""
    if name != "example-prompt":
        raise ValueError(f"Unknown prompt: {name}")

    arg1_value = (arguments or {}).get("arg1", "default")

    return types.GetPromptResult(
        description="Example prompt",
        messages=[
            types.PromptMessage(
                role="user",
                content=types.TextContent(type="text", text=f"Example prompt text with argument: {arg1_value}"),
            )
        ],
    )


async def run():
    """Run the basic low-level server."""
    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__":
    asyncio.run(run())

Full example: examples/snippets/servers/lowlevel/basic.py

Caution: The uv run mcp run and uv run 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:

"""
Run from the repository root:
    uv run examples/snippets/servers/lowlevel/structured_output.py
"""

import asyncio
from typing import Any

import mcp.server.stdio
import mcp.types as types
from mcp.server.lowlevel import NotificationOptions, Server
from mcp.server.models import InitializationOptions

server = Server("example-server")


@server.list_tools()
async def list_tools() -> list[types.Tool]:
    """List available tools with structured output schemas."""
    return [
        types.Tool(
            name="get_weather",
            description="Get current weather for a city",
            inputSchema={
                "type": "object",
                "properties": {"city": {"type": "string", "description": "City name"}},
                "required": ["city"],
            },
            outputSchema={
                "type": "object",
                "properties": {
                    "temperature": {"type": "number", "description": "Temperature in Celsius"},
                    "condition": {"type": "string", "description": "Weather condition"},
                    "humidity": {"type": "number", "description": "Humidity percentage"},
                    "city": {"type": "string", "description": "City name"},
                },
                "required": ["temperature", "condition", "humidity", "city"],
            },
        )
    ]


@server.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> dict[str, Any]:
    """Handle tool calls with structured output."""
    if name == "get_weather":
        city = arguments["city"]

        # Simulated weather data - in production, call a weather API
        weather_data = {
            "temperature": 22.5,
            "condition": "partly cloudy",
            "humidity": 65,
            "city": city,  # Include the requested city
        }

        # low-level server will validate structured output against the tool's
        # output schema, and additionally serialize it into a TextContent block
        # for backwards compatibility with pre-2025-06-18 clients.
        return weather_data
    else:
        raise ValueError(f"Unknown tool: {name}")


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


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

Full example: examples/snippets/servers/lowlevel/structured_output.py

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:

"""
cd to the `examples/snippets/clients` directory and run:
    uv run client
"""

import asyncio
import os

from pydantic import AnyUrl

from mcp import ClientSession, StdioServerParameters, types
from mcp.client.stdio import stdio_client
from mcp.shared.context import RequestContext

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="uv",  # Using uv to run the server
    args=["run", "server", "fastmcp_quickstart", "stdio"],  # We're already in snippets dir
    env={"UV_INDEX": os.environ.get("UV_INDEX", "")},
)


# Optional: create a sampling callback
async def handle_sampling_message(
    context: RequestContext[ClientSession, None], params: types.CreateMessageRequestParams
) -> types.CreateMessageResult:
    print(f"Sampling request: {params.messages}")
    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()
            print(f"Available prompts: {[p.name for p in prompts.prompts]}")

            # Get a prompt (greet_user prompt from fastmcp_quickstart)
            if prompts.prompts:
                prompt = await session.get_prompt("greet_user", arguments={"name": "Alice", "style": "friendly"})
                print(f"Prompt result: {prompt.messages[0].content}")

            # List available resources
            resources = await session.list_resources()
            print(f"Available resources: {[r.uri for r in resources.resources]}")

            # List available tools
            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

            # Read a resource (greeting resource from fastmcp_quickstart)
            resource_content = await session.read_resource(AnyUrl("greeting://World"))
            content_block = resource_content.contents[0]
            if isinstance(content_block, types.TextContent):
                print(f"Resource content: {content_block.text}")

            # Call a tool (add tool from fastmcp_quickstart)
            result = await session.call_tool("add", arguments={"a": 5, "b": 3})
            result_unstructured = result.content[0]
            if isinstance(result_unstructured, types.TextContent):
                print(f"Tool result: {result_unstructured.text}")
            result_structured = result.structuredContent
            print(f"Structured tool result: {result_structured}")


def main():
    """Entry point for the client script."""
    asyncio.run(run())


if __name__ == "__main__":
    main()

Full example: examples/snippets/clients/stdio_client.py

Clients can also connect using Streamable HTTP transport:

"""
Run from the repository root:
    uv run examples/snippets/clients/streamable_basic.py
"""

import asyncio

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client


async def main():
    # Connect to a streamable HTTP server
    async with streamablehttp_client("http://localhost:8000/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()
            # List available tools
            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")


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

Full example: examples/snippets/clients/streamable_basic.py

Client Display Utilities

When building MCP clients, the SDK provides utilities to help display human-readable names for tools, resources, and prompts:

"""
cd to the `examples/snippets` directory and run:
    uv run display-utilities-client
"""

import asyncio
import os

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from mcp.shared.metadata_utils import get_display_name

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="uv",  # Using uv to run the server
    args=["run", "server", "fastmcp_quickstart", "stdio"],
    env={"UV_INDEX": os.environ.get("UV_INDEX", "")},
)


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})")

    templates_response = await session.list_resource_templates()
    for template in templates_response.resourceTemplates:
        display_name = get_display_name(template)
        print(f"Resource Template: {display_name}")


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

            print("=== Available Tools ===")
            await display_tools(session)

            print("\n=== Available Resources ===")
            await display_resources(session)


def main():
    """Entry point for the display utilities client."""
    asyncio.run(run())


if __name__ == "__main__":
    main()

Full example: examples/snippets/clients/display_utilities.py

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:

"""
Before running, specify running MCP RS server URL.
To spin up RS server locally, see
    examples/servers/simple-auth/README.md

cd to the `examples/snippets` directory and run:
    uv run oauth-client
"""

import asyncio
from urllib.parse import parse_qs, urlparse

from pydantic import AnyUrl

from mcp import ClientSession
from mcp.client.auth import OAuthClientProvider, TokenStorage
from mcp.client.streamable_http import streamablehttp_client
from mcp.shared.auth import OAuthClientInformationFull, OAuthClientMetadata, OAuthToken


class InMemoryTokenStorage(TokenStorage):
    """Demo In-memory token storage implementation."""

    def __init__(self):
        self.tokens: OAuthToken | None = None
        self.client_info: OAuthClientInformationFull | None = None

    async def get_tokens(self) -> OAuthToken | None:
        """Get stored tokens."""
        return self.tokens

    async def set_tokens(self, tokens: OAuthToken) -> None:
        """Store tokens."""
        self.tokens = tokens

    async def get_client_info(self) -> OAuthClientInformationFull | None:
        """Get stored client information."""
        return self.client_info

    async def set_client_info(self, client_info: OAuthClientInformationFull) -> None:
        """Store client information."""
        self.client_info = client_info


async def handle_redirect(auth_url: str) -> None:
    print(f"Visit: {auth_url}")


async def handle_callback() -> tuple[str, str | None]:
    callback_url = input("Paste callback URL: ")
    params = parse_qs(urlparse(callback_url).query)
    return params["code"][0], params.get("state", [None])[0]


async def main():
    """Run the OAuth client example."""
    oauth_auth = OAuthClientProvider(
        server_url="http://localhost:8001",
        client_metadata=OAuthClientMetadata(
            client_name="Example MCP Client",
            redirect_uris=[AnyUrl("http://localhost:3000/callback")],
            grant_types=["authorization_code", "refresh_token"],
            response_types=["code"],
            scope="user",
        ),
        storage=InMemoryTokenStorage(),
        redirect_handler=handle_redirect,
        callback_handler=handle_callback,
    )

    async with streamablehttp_client("http://localhost:8001/mcp", auth=oauth_auth) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")

            resources = await session.list_resources()
            print(f"Available resources: {[r.uri for r in resources.resources]}")


def run():
    asyncio.run(main())


if __name__ == "__main__":
    run()

Full example: examples/snippets/clients/oauth_client.py

For a complete working example, see examples/clients/simple-auth-client/.

Parsing Tool Results

When calling tools through MCP, the CallToolResult object contains the tool's response in a structured format. Understanding how to parse this result is essential for properly handling tool outputs.

"""examples/snippets/clients/parsing_tool_results.py"""

import asyncio

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


async def parse_tool_results():
    """Demonstrates how to parse different types of content in CallToolResult."""
    server_params = StdioServerParameters(
        command="python", args=["path/to/mcp_server.py"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # Example 1: Parsing text content
            result = await session.call_tool("get_data", {"format": "text"})
            for content in result.content:
                if isinstance(content, types.TextContent):
                    print(f"Text: {content.text}")

            # Example 2: Parsing structured content from JSON tools
            result = await session.call_tool("get_user", {"id": "123"})
            if hasattr(result, "structuredContent") and result.structuredContent:
                # Access structured data directly
                user_data = result.structuredContent
                print(f"User: {user_data.get('name')}, Age: {user_data.get('age')}")

            # Example 3: Parsing embedded resources
            result = await session.call_tool("read_config", {})
            for content in result.content:
                if isinstance(content, types.EmbeddedResource):
                    resource = content.resource
                    if isinstance(resource, types.TextResourceContents):
                        print(f"Config from {resource.uri}: {resource.text}")
                    elif isinstance(resource, types.BlobResourceContents):
                        print(f"Binary data from {resource.uri}")

            # Example 4: Parsing image content
            result = await session.call_tool("generate_chart", {"data": [1, 2, 3]})
            for content in result.content:
                if isinstance(content, types.ImageContent):
                    print(f"Image ({content.mimeType}): {len(content.data)} bytes")

            # Example 5: Handling errors
            result = await session.call_tool("failing_tool", {})
            if result.isError:
                print("Tool execution failed!")
                for content in result.content:
                    if isinstance(content, types.TextContent):
                        print(f"Error: {content.text}")


async def main():
    await parse_tool_results()


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

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`	`subscribe` `listChanged`	Resource exposure and updates
`tools`	`listChanged`	Tool discovery and execution
`logging`	-	Server logging configuration
`completions`	-	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.

License

modelcontextprotocol/python-sdk

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