pptx-extractor-mcp 0.1.0
MCP server for extracting PPTX files to Marp format
Requires Python: >=3.10
Keywords: MCP, extractor, marp, pptx

# 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)
      - [Structured Output](#structured-output)
    - [Prompts](#prompts)
    - [Images](#images)
    - [Context](#context)
    - [Completions](#completions)
    - [Elicitation](#elicitation)
    - [Sampling](#sampling)
    - [Logging and Notifications](#logging-and-notifications)
    - [Authentication](#authentication)
  - [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)
  - [Advanced Usage](#advanced-usage)
    - [Low-Level Server](#low-level-server)
    - [Writing MCP Clients](#writing-mcp-clients)
    - [Parsing Tool Results](#parsing-tool-results)
    - [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, SSE, and Streamable HTTP
- 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.

If you haven't created a uv-managed project yet, create one:

   ```bash
   uv init mcp-server-demo
   cd mcp-server-demo
   ```

   Then add MCP to your project dependencies:

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

Alternatively, for projects using pip for dependencies:

```bash
pip install "mcp[cli]"
```

### 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:

<!-- snippet-source examples/snippets/servers/fastmcp_quickstart.py -->
```python
"""
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/fastmcp_quickstart.py)_
<!-- /snippet-source -->

You can install this server in [Claude Desktop](https://claude.ai/download) and interact with it right away by running:

```bash
uv run mcp install server.py
```

Alternatively, you can test it with the MCP Inspector:

```bash
uv run 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:

<!-- snippet-source examples/snippets/servers/lifespan_example.py -->
```python
"""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


# 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) -> str:
    """Tool that uses initialized resources."""
    db = ctx.request_context.lifespan_context.db
    return db.query()
```

_Full example: [examples/snippets/servers/lifespan_example.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/lifespan_example.py)_
<!-- /snippet-source -->

### 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:

<!-- snippet-source examples/snippets/servers/basic_resource.py -->
```python
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_resource.py)_
<!-- /snippet-source -->

### Tools

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

<!-- snippet-source examples/snippets/servers/basic_tool.py -->
```python
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_tool.py)_
<!-- /snippet-source -->

#### 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.

<!-- snippet-source examples/snippets/servers/structured_output.py -->
```python
"""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=72.5,
        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):
        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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/structured_output.py)_
<!-- /snippet-source -->

### Prompts

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

<!-- snippet-source examples/snippets/servers/basic_prompt.py -->
```python
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/basic_prompt.py)_
<!-- /snippet-source -->

### Images

FastMCP provides an `Image` class that automatically handles image data:

<!-- snippet-source examples/snippets/servers/images.py -->
```python
"""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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/images.py)_
<!-- /snippet-source -->

### Context

The Context object gives your tools and resources access to MCP capabilities:

<!-- snippet-source examples/snippets/servers/tool_progress.py -->
```python
from mcp.server.fastmcp import Context, FastMCP

mcp = FastMCP(name="Progress Example")


@mcp.tool()
async def long_running_task(task_name: str, ctx: Context, 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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/tool_progress.py)_
<!-- /snippet-source -->

### 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:

<!-- snippet-source examples/snippets/clients/completion_client.py -->
```python
"""
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/clients/completion_client.py)_
<!-- /snippet-source -->
### Elicitation

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

<!-- snippet-source examples/snippets/servers/elicitation.py -->
```python
from pydantic import BaseModel, Field

from mcp.server.fastmcp import Context, FastMCP

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,
) -> 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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/elicitation.py)_
<!-- /snippet-source -->

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):

<!-- snippet-source examples/snippets/servers/sampling.py -->
```python
from mcp.server.fastmcp import Context, FastMCP
from mcp.types import SamplingMessage, TextContent

mcp = FastMCP(name="Sampling Example")


@mcp.tool()
async def generate_poem(topic: str, ctx: Context) -> 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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/sampling.py)_
<!-- /snippet-source -->

### Logging and Notifications

Tools can send logs and notifications through the context:

<!-- snippet-source examples/snippets/servers/notifications.py -->
```python
from mcp.server.fastmcp import Context, FastMCP

mcp = FastMCP(name="Notifications Example")


@mcp.tool()
async def process_data(data: str, ctx: Context) -> 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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/notifications.py)_
<!-- /snippet-source -->

### 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](https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization) and implements RFC 9728 (Protected Resource Metadata) for AS discovery.

MCP servers can use authentication by providing an implementation of the `TokenVerifier` protocol:

<!-- snippet-source examples/snippets/servers/oauth_server.py -->
```python
"""
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/oauth_server.py)_
<!-- /snippet-source -->

For a complete example with separate Authorization Server and Resource Server implementations, see [`examples/servers/simple-auth/`](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](src/mcp/server/auth/provider.py) for more details on implementing token validation.

## Running Your Server

### Development Mode

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

```bash
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:

```bash
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:

<!-- snippet-source examples/snippets/servers/direct_execution.py -->
```python
"""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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/direct_execution.py)_
<!-- /snippet-source -->

Run it with:

```bash
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.

<!-- snippet-source examples/snippets/servers/streamable_config.py -->
```python
"""
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/streamable_config.py)_
<!-- /snippet-source -->

You can mount multiple FastMCP servers in a Starlette application:

<!-- snippet-source examples/snippets/servers/streamable_starlette_mount.py -->
```python
"""
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,
)
```

_Full example: [examples/snippets/servers/streamable_starlette_mount.py](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/streamable_starlette_mount.py)_
<!-- /snippet-source -->

For low level server with Streamable HTTP implementations, see:

- Stateful server: [`examples/servers/simple-streamablehttp/`](examples/servers/simple-streamablehttp/)
- Stateless server: [`examples/servers/simple-streamablehttp-stateless/`](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

### 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](https://www.starlette.io/routing/#submounting-routes).

#### SSE servers

> **Note**: SSE transport is being superseded by [Streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http).

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.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:

```python
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](https://www.starlette.io/routing/#submounting-routes).

## 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:

<!-- snippet-source examples/snippets/servers/lowlevel/lifespan.py -->
```python
"""
Run from the repository root:
    uv run examples/snippets/servers/lowlevel/lifespan.py
"""

from collections.abc import AsyncIterator
from contextlib import asynccontextmanager

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]:
    """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) -> 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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/lowlevel/lifespan.py)_
<!-- /snippet-source -->

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

<!-- snippet-source examples/snippets/servers/lowlevel/basic.py -->
```python
"""
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](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/lowlevel/basic.py)_
<!-- /snippet-source -->

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 struct