mcp


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authorAnthropic, PBC.
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            # MCP Python SDK

<div align="center">

<strong>Python implementation of the Model Context Protocol (MCP)</strong>

[![PyPI][pypi-badge]][pypi-url]
[![MIT licensed][mit-badge]][mit-url]
[![Python Version][python-badge]][python-url]
[![Documentation][docs-badge]][docs-url]
[![Specification][spec-badge]][spec-url]
[![GitHub Discussions][discussions-badge]][discussions-url]

</div>

<!-- omit in toc -->
## Table of Contents

- [MCP Python SDK](#mcp-python-sdk)
  - [Overview](#overview)
  - [Installation](#installation)
    - [Adding MCP to your python project](#adding-mcp-to-your-python-project)
    - [Running the standalone MCP development tools](#running-the-standalone-mcp-development-tools)
  - [Quickstart](#quickstart)
  - [What is MCP?](#what-is-mcp)
  - [Core Concepts](#core-concepts)
    - [Server](#server)
    - [Resources](#resources)
    - [Tools](#tools)
    - [Prompts](#prompts)
    - [Images](#images)
    - [Context](#context)
  - [Running Your Server](#running-your-server)
    - [Development Mode](#development-mode)
    - [Claude Desktop Integration](#claude-desktop-integration)
    - [Direct Execution](#direct-execution)
    - [Mounting to an Existing ASGI Server](#mounting-to-an-existing-asgi-server)
  - [Examples](#examples)
    - [Echo Server](#echo-server)
    - [SQLite Explorer](#sqlite-explorer)
  - [Advanced Usage](#advanced-usage)
    - [Low-Level Server](#low-level-server)
    - [Writing MCP Clients](#writing-mcp-clients)
    - [MCP Primitives](#mcp-primitives)
    - [Server Capabilities](#server-capabilities)
  - [Documentation](#documentation)
  - [Contributing](#contributing)
  - [License](#license)

[pypi-badge]: https://img.shields.io/pypi/v/mcp.svg
[pypi-url]: https://pypi.org/project/mcp/
[mit-badge]: https://img.shields.io/pypi/l/mcp.svg
[mit-url]: https://github.com/modelcontextprotocol/python-sdk/blob/main/LICENSE
[python-badge]: https://img.shields.io/pypi/pyversions/mcp.svg
[python-url]: https://www.python.org/downloads/
[docs-badge]: https://img.shields.io/badge/docs-modelcontextprotocol.io-blue.svg
[docs-url]: https://modelcontextprotocol.io
[spec-badge]: https://img.shields.io/badge/spec-spec.modelcontextprotocol.io-blue.svg
[spec-url]: https://spec.modelcontextprotocol.io
[discussions-badge]: https://img.shields.io/github/discussions/modelcontextprotocol/python-sdk
[discussions-url]: https://github.com/modelcontextprotocol/python-sdk/discussions

## 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 and SSE
- Handle all MCP protocol messages and lifecycle events

## Installation

### Adding MCP to your python project

We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects. In a uv managed python project, add mcp to dependencies by:

```bash
uv add "mcp[cli]"
```

Alternatively, for projects using pip for dependencies:
```bash
pip install mcp
```

### Running the standalone MCP development tools

To run the mcp command with uv:

```bash
uv run mcp
```

## Quickstart

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

```python
# 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](https://claude.ai/download) and interact with it right away by running:
```bash
mcp install server.py
```

Alternatively, you can test it with the MCP Inspector:
```bash
mcp dev server.py
```

## What is MCP?

The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) 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:

```python
# 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 Context, 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(ctx: Context) -> str:
    """Tool that uses initialized resources"""
    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:

```python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")


@mcp.resource("config://app")
def get_config() -> str:
    """Static configuration data"""
    return "App configuration here"


@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> str:
    """Dynamic user data"""
    return f"Profile data for user {user_id}"
```

### Tools

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

```python
import httpx
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")


@mcp.tool()
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()
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
```

### Prompts

Prompts are reusable templates that help LLMs interact with your server effectively:

```python
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base

mcp = FastMCP("My App")


@mcp.prompt()
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"


@mcp.prompt()
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:

```python
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:

```python
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"
```

## Running Your Server

### Development Mode

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

```bash
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:

```bash
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:

```python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")

if __name__ == "__main__":
    mcp.run()
```

Run it with:
```bash
python server.py
# or
mcp run server.py
```

### Mounting to an Existing ASGI Server

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.

```python
from starlette.applications import Starlette
from starlette.routes 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()))
```

For more information on mounting applications in Starlette, see the [Starlette documentation](https://www.starlette.io/routing/#submounting-routes).

## Examples

### Echo Server

A simple server demonstrating resources, tools, and prompts:

```python
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:

```python
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:

```python
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

```python
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())
```

### Writing MCP Clients

The SDK provides a high-level client interface for connecting to MCP servers:

```python
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())
```

### 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`<br/>`listChanged`| Resource exposure and updates      |
| `tools`     | `listChanged`                | Tool discovery and execution       |
| `logging`   | -                            | Server logging configuration       |
| `completion`| -                            | Argument completion suggestions    |

## Documentation

- [Model Context Protocol documentation](https://modelcontextprotocol.io)
- [Model Context Protocol specification](https://spec.modelcontextprotocol.io)
- [Officially supported servers](https://github.com/modelcontextprotocol/servers)

## 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](CONTRIBUTING.md) to get started.

## License

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

            

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    "description": "# MCP Python SDK\n\n<div align=\"center\">\n\n<strong>Python implementation of the Model Context Protocol (MCP)</strong>\n\n[![PyPI][pypi-badge]][pypi-url]\n[![MIT licensed][mit-badge]][mit-url]\n[![Python Version][python-badge]][python-url]\n[![Documentation][docs-badge]][docs-url]\n[![Specification][spec-badge]][spec-url]\n[![GitHub Discussions][discussions-badge]][discussions-url]\n\n</div>\n\n<!-- omit in toc -->\n## Table of Contents\n\n- [MCP Python SDK](#mcp-python-sdk)\n  - [Overview](#overview)\n  - [Installation](#installation)\n    - [Adding MCP to your python project](#adding-mcp-to-your-python-project)\n    - [Running the standalone MCP development tools](#running-the-standalone-mcp-development-tools)\n  - [Quickstart](#quickstart)\n  - [What is MCP?](#what-is-mcp)\n  - [Core Concepts](#core-concepts)\n    - [Server](#server)\n    - [Resources](#resources)\n    - [Tools](#tools)\n    - [Prompts](#prompts)\n    - [Images](#images)\n    - [Context](#context)\n  - [Running Your Server](#running-your-server)\n    - [Development Mode](#development-mode)\n    - [Claude Desktop Integration](#claude-desktop-integration)\n    - [Direct Execution](#direct-execution)\n    - [Mounting to an Existing ASGI Server](#mounting-to-an-existing-asgi-server)\n  - [Examples](#examples)\n    - [Echo Server](#echo-server)\n    - [SQLite Explorer](#sqlite-explorer)\n  - [Advanced Usage](#advanced-usage)\n    - [Low-Level Server](#low-level-server)\n    - [Writing MCP Clients](#writing-mcp-clients)\n    - [MCP Primitives](#mcp-primitives)\n    - [Server Capabilities](#server-capabilities)\n  - [Documentation](#documentation)\n  - [Contributing](#contributing)\n  - [License](#license)\n\n[pypi-badge]: https://img.shields.io/pypi/v/mcp.svg\n[pypi-url]: https://pypi.org/project/mcp/\n[mit-badge]: https://img.shields.io/pypi/l/mcp.svg\n[mit-url]: https://github.com/modelcontextprotocol/python-sdk/blob/main/LICENSE\n[python-badge]: https://img.shields.io/pypi/pyversions/mcp.svg\n[python-url]: https://www.python.org/downloads/\n[docs-badge]: https://img.shields.io/badge/docs-modelcontextprotocol.io-blue.svg\n[docs-url]: https://modelcontextprotocol.io\n[spec-badge]: https://img.shields.io/badge/spec-spec.modelcontextprotocol.io-blue.svg\n[spec-url]: https://spec.modelcontextprotocol.io\n[discussions-badge]: https://img.shields.io/github/discussions/modelcontextprotocol/python-sdk\n[discussions-url]: https://github.com/modelcontextprotocol/python-sdk/discussions\n\n## Overview\n\nThe 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:\n\n- Build MCP clients that can connect to any MCP server\n- Create MCP servers that expose resources, prompts and tools\n- Use standard transports like stdio and SSE\n- Handle all MCP protocol messages and lifecycle events\n\n## Installation\n\n### Adding MCP to your python project\n\nWe recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects. In a uv managed python project, add mcp to dependencies by:\n\n```bash\nuv add \"mcp[cli]\"\n```\n\nAlternatively, for projects using pip for dependencies:\n```bash\npip install mcp\n```\n\n### Running the standalone MCP development tools\n\nTo run the mcp command with uv:\n\n```bash\nuv run mcp\n```\n\n## Quickstart\n\nLet's create a simple MCP server that exposes a calculator tool and some data:\n\n```python\n# server.py\nfrom mcp.server.fastmcp import FastMCP\n\n# Create an MCP server\nmcp = FastMCP(\"Demo\")\n\n\n# Add an addition tool\n@mcp.tool()\ndef add(a: int, b: int) -> int:\n    \"\"\"Add two numbers\"\"\"\n    return a + b\n\n\n# Add a dynamic greeting resource\n@mcp.resource(\"greeting://{name}\")\ndef get_greeting(name: str) -> str:\n    \"\"\"Get a personalized greeting\"\"\"\n    return f\"Hello, {name}!\"\n```\n\nYou can install this server in [Claude Desktop](https://claude.ai/download) and interact with it right away by running:\n```bash\nmcp install server.py\n```\n\nAlternatively, you can test it with the MCP Inspector:\n```bash\nmcp dev server.py\n```\n\n## What is MCP?\n\nThe [Model Context Protocol (MCP)](https://modelcontextprotocol.io) 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:\n\n- Expose data through **Resources** (think of these sort of like GET endpoints; they are used to load information into the LLM's context)\n- Provide functionality through **Tools** (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)\n- Define interaction patterns through **Prompts** (reusable templates for LLM interactions)\n- And more!\n\n## Core Concepts\n\n### Server\n\nThe FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:\n\n```python\n# Add lifespan support for startup/shutdown with strong typing\nfrom contextlib import asynccontextmanager\nfrom collections.abc import AsyncIterator\nfrom dataclasses import dataclass\n\nfrom fake_database import Database  # Replace with your actual DB type\n\nfrom mcp.server.fastmcp import Context, FastMCP\n\n# Create a named server\nmcp = FastMCP(\"My App\")\n\n# Specify dependencies for deployment and development\nmcp = FastMCP(\"My App\", dependencies=[\"pandas\", \"numpy\"])\n\n\n@dataclass\nclass AppContext:\n    db: Database\n\n\n@asynccontextmanager\nasync def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:\n    \"\"\"Manage application lifecycle with type-safe context\"\"\"\n    # Initialize on startup\n    db = await Database.connect()\n    try:\n        yield AppContext(db=db)\n    finally:\n        # Cleanup on shutdown\n        await db.disconnect()\n\n\n# Pass lifespan to server\nmcp = FastMCP(\"My App\", lifespan=app_lifespan)\n\n\n# Access type-safe lifespan context in tools\n@mcp.tool()\ndef query_db(ctx: Context) -> str:\n    \"\"\"Tool that uses initialized resources\"\"\"\n    db = ctx.request_context.lifespan_context[\"db\"]\n    return db.query()\n```\n\n### Resources\n\nResources 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:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"My App\")\n\n\n@mcp.resource(\"config://app\")\ndef get_config() -> str:\n    \"\"\"Static configuration data\"\"\"\n    return \"App configuration here\"\n\n\n@mcp.resource(\"users://{user_id}/profile\")\ndef get_user_profile(user_id: str) -> str:\n    \"\"\"Dynamic user data\"\"\"\n    return f\"Profile data for user {user_id}\"\n```\n\n### Tools\n\nTools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:\n\n```python\nimport httpx\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"My App\")\n\n\n@mcp.tool()\ndef calculate_bmi(weight_kg: float, height_m: float) -> float:\n    \"\"\"Calculate BMI given weight in kg and height in meters\"\"\"\n    return weight_kg / (height_m**2)\n\n\n@mcp.tool()\nasync def fetch_weather(city: str) -> str:\n    \"\"\"Fetch current weather for a city\"\"\"\n    async with httpx.AsyncClient() as client:\n        response = await client.get(f\"https://api.weather.com/{city}\")\n        return response.text\n```\n\n### Prompts\n\nPrompts are reusable templates that help LLMs interact with your server effectively:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom mcp.server.fastmcp.prompts import base\n\nmcp = FastMCP(\"My App\")\n\n\n@mcp.prompt()\ndef review_code(code: str) -> str:\n    return f\"Please review this code:\\n\\n{code}\"\n\n\n@mcp.prompt()\ndef debug_error(error: str) -> list[base.Message]:\n    return [\n        base.UserMessage(\"I'm seeing this error:\"),\n        base.UserMessage(error),\n        base.AssistantMessage(\"I'll help debug that. What have you tried so far?\"),\n    ]\n```\n\n### Images\n\nFastMCP provides an `Image` class that automatically handles image data:\n\n```python\nfrom mcp.server.fastmcp import FastMCP, Image\nfrom PIL import Image as PILImage\n\nmcp = FastMCP(\"My App\")\n\n\n@mcp.tool()\ndef create_thumbnail(image_path: str) -> Image:\n    \"\"\"Create a thumbnail from an image\"\"\"\n    img = PILImage.open(image_path)\n    img.thumbnail((100, 100))\n    return Image(data=img.tobytes(), format=\"png\")\n```\n\n### Context\n\nThe Context object gives your tools and resources access to MCP capabilities:\n\n```python\nfrom mcp.server.fastmcp import FastMCP, Context\n\nmcp = FastMCP(\"My App\")\n\n\n@mcp.tool()\nasync def long_task(files: list[str], ctx: Context) -> str:\n    \"\"\"Process multiple files with progress tracking\"\"\"\n    for i, file in enumerate(files):\n        ctx.info(f\"Processing {file}\")\n        await ctx.report_progress(i, len(files))\n        data, mime_type = await ctx.read_resource(f\"file://{file}\")\n    return \"Processing complete\"\n```\n\n## Running Your Server\n\n### Development Mode\n\nThe fastest way to test and debug your server is with the MCP Inspector:\n\n```bash\nmcp dev server.py\n\n# Add dependencies\nmcp dev server.py --with pandas --with numpy\n\n# Mount local code\nmcp dev server.py --with-editable .\n```\n\n### Claude Desktop Integration\n\nOnce your server is ready, install it in Claude Desktop:\n\n```bash\nmcp install server.py\n\n# Custom name\nmcp install server.py --name \"My Analytics Server\"\n\n# Environment variables\nmcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...\nmcp install server.py -f .env\n```\n\n### Direct Execution\n\nFor advanced scenarios like custom deployments:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"My App\")\n\nif __name__ == \"__main__\":\n    mcp.run()\n```\n\nRun it with:\n```bash\npython server.py\n# or\nmcp run server.py\n```\n\n### Mounting to an Existing ASGI Server\n\nYou 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.\n\n```python\nfrom starlette.applications import Starlette\nfrom starlette.routes import Mount, Host\nfrom mcp.server.fastmcp import FastMCP\n\n\nmcp = FastMCP(\"My App\")\n\n# Mount the SSE server to the existing ASGI server\napp = Starlette(\n    routes=[\n        Mount('/', app=mcp.sse_app()),\n    ]\n)\n\n# or dynamically mount as host\napp.router.routes.append(Host('mcp.acme.corp', app=mcp.sse_app()))\n```\n\nFor more information on mounting applications in Starlette, see the [Starlette documentation](https://www.starlette.io/routing/#submounting-routes).\n\n## Examples\n\n### Echo Server\n\nA simple server demonstrating resources, tools, and prompts:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"Echo\")\n\n\n@mcp.resource(\"echo://{message}\")\ndef echo_resource(message: str) -> str:\n    \"\"\"Echo a message as a resource\"\"\"\n    return f\"Resource echo: {message}\"\n\n\n@mcp.tool()\ndef echo_tool(message: str) -> str:\n    \"\"\"Echo a message as a tool\"\"\"\n    return f\"Tool echo: {message}\"\n\n\n@mcp.prompt()\ndef echo_prompt(message: str) -> str:\n    \"\"\"Create an echo prompt\"\"\"\n    return f\"Please process this message: {message}\"\n```\n\n### SQLite Explorer\n\nA more complex example showing database integration:\n\n```python\nimport sqlite3\n\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"SQLite Explorer\")\n\n\n@mcp.resource(\"schema://main\")\ndef get_schema() -> str:\n    \"\"\"Provide the database schema as a resource\"\"\"\n    conn = sqlite3.connect(\"database.db\")\n    schema = conn.execute(\"SELECT sql FROM sqlite_master WHERE type='table'\").fetchall()\n    return \"\\n\".join(sql[0] for sql in schema if sql[0])\n\n\n@mcp.tool()\ndef query_data(sql: str) -> str:\n    \"\"\"Execute SQL queries safely\"\"\"\n    conn = sqlite3.connect(\"database.db\")\n    try:\n        result = conn.execute(sql).fetchall()\n        return \"\\n\".join(str(row) for row in result)\n    except Exception as e:\n        return f\"Error: {str(e)}\"\n```\n\n## Advanced Usage\n\n### Low-Level Server\n\nFor 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:\n\n```python\nfrom contextlib import asynccontextmanager\nfrom collections.abc import AsyncIterator\n\nfrom fake_database import Database  # Replace with your actual DB type\n\nfrom mcp.server import Server\n\n\n@asynccontextmanager\nasync def server_lifespan(server: Server) -> AsyncIterator[dict]:\n    \"\"\"Manage server startup and shutdown lifecycle.\"\"\"\n    # Initialize resources on startup\n    db = await Database.connect()\n    try:\n        yield {\"db\": db}\n    finally:\n        # Clean up on shutdown\n        await db.disconnect()\n\n\n# Pass lifespan to server\nserver = Server(\"example-server\", lifespan=server_lifespan)\n\n\n# Access lifespan context in handlers\n@server.call_tool()\nasync def query_db(name: str, arguments: dict) -> list:\n    ctx = server.request_context\n    db = ctx.lifespan_context[\"db\"]\n    return await db.query(arguments[\"query\"])\n```\n\nThe lifespan API provides:\n- A way to initialize resources when the server starts and clean them up when it stops\n- Access to initialized resources through the request context in handlers\n- Type-safe context passing between lifespan and request handlers\n\n```python\nimport mcp.server.stdio\nimport mcp.types as types\nfrom mcp.server.lowlevel import NotificationOptions, Server\nfrom mcp.server.models import InitializationOptions\n\n# Create a server instance\nserver = Server(\"example-server\")\n\n\n@server.list_prompts()\nasync def handle_list_prompts() -> list[types.Prompt]:\n    return [\n        types.Prompt(\n            name=\"example-prompt\",\n            description=\"An example prompt template\",\n            arguments=[\n                types.PromptArgument(\n                    name=\"arg1\", description=\"Example argument\", required=True\n                )\n            ],\n        )\n    ]\n\n\n@server.get_prompt()\nasync def handle_get_prompt(\n    name: str, arguments: dict[str, str] | None\n) -> types.GetPromptResult:\n    if name != \"example-prompt\":\n        raise ValueError(f\"Unknown prompt: {name}\")\n\n    return types.GetPromptResult(\n        description=\"Example prompt\",\n        messages=[\n            types.PromptMessage(\n                role=\"user\",\n                content=types.TextContent(type=\"text\", text=\"Example prompt text\"),\n            )\n        ],\n    )\n\n\nasync def run():\n    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):\n        await server.run(\n            read_stream,\n            write_stream,\n            InitializationOptions(\n                server_name=\"example\",\n                server_version=\"0.1.0\",\n                capabilities=server.get_capabilities(\n                    notification_options=NotificationOptions(),\n                    experimental_capabilities={},\n                ),\n            ),\n        )\n\n\nif __name__ == \"__main__\":\n    import asyncio\n\n    asyncio.run(run())\n```\n\n### Writing MCP Clients\n\nThe SDK provides a high-level client interface for connecting to MCP servers:\n\n```python\nfrom mcp import ClientSession, StdioServerParameters, types\nfrom mcp.client.stdio import stdio_client\n\n# Create server parameters for stdio connection\nserver_params = StdioServerParameters(\n    command=\"python\",  # Executable\n    args=[\"example_server.py\"],  # Optional command line arguments\n    env=None,  # Optional environment variables\n)\n\n\n# Optional: create a sampling callback\nasync def handle_sampling_message(\n    message: types.CreateMessageRequestParams,\n) -> types.CreateMessageResult:\n    return types.CreateMessageResult(\n        role=\"assistant\",\n        content=types.TextContent(\n            type=\"text\",\n            text=\"Hello, world! from model\",\n        ),\n        model=\"gpt-3.5-turbo\",\n        stopReason=\"endTurn\",\n    )\n\n\nasync def run():\n    async with stdio_client(server_params) as (read, write):\n        async with ClientSession(\n            read, write, sampling_callback=handle_sampling_message\n        ) as session:\n            # Initialize the connection\n            await session.initialize()\n\n            # List available prompts\n            prompts = await session.list_prompts()\n\n            # Get a prompt\n            prompt = await session.get_prompt(\n                \"example-prompt\", arguments={\"arg1\": \"value\"}\n            )\n\n            # List available resources\n            resources = await session.list_resources()\n\n            # List available tools\n            tools = await session.list_tools()\n\n            # Read a resource\n            content, mime_type = await session.read_resource(\"file://some/path\")\n\n            # Call a tool\n            result = await session.call_tool(\"tool-name\", arguments={\"arg1\": \"value\"})\n\n\nif __name__ == \"__main__\":\n    import asyncio\n\n    asyncio.run(run())\n```\n\n### MCP Primitives\n\nThe MCP protocol defines three core primitives that servers can implement:\n\n| Primitive | Control               | Description                                         | Example Use                  |\n|-----------|-----------------------|-----------------------------------------------------|------------------------------|\n| Prompts   | User-controlled       | Interactive templates invoked by user choice        | Slash commands, menu options |\n| Resources | Application-controlled| Contextual data managed by the client application   | File contents, API responses |\n| Tools     | Model-controlled      | Functions exposed to the LLM to take actions        | API calls, data updates      |\n\n### Server Capabilities\n\nMCP servers declare capabilities during initialization:\n\n| Capability  | Feature Flag                 | Description                        |\n|-------------|------------------------------|------------------------------------|\n| `prompts`   | `listChanged`                | Prompt template management         |\n| `resources` | `subscribe`<br/>`listChanged`| Resource exposure and updates      |\n| `tools`     | `listChanged`                | Tool discovery and execution       |\n| `logging`   | -                            | Server logging configuration       |\n| `completion`| -                            | Argument completion suggestions    |\n\n## Documentation\n\n- [Model Context Protocol documentation](https://modelcontextprotocol.io)\n- [Model Context Protocol specification](https://spec.modelcontextprotocol.io)\n- [Officially supported servers](https://github.com/modelcontextprotocol/servers)\n\n## Contributing\n\nWe 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](CONTRIBUTING.md) to get started.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n",
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