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### 🎉 FastMCP has been added to the official MCP SDK! 🎉
You can now find FastMCP as part of the official Model Context Protocol Python SDK:
👉 [github.com/modelcontextprotocol/python-sdk](https://github.com/modelcontextprotocol/python-sdk)
*Please note: this repository is no longer maintained.*
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# FastMCP 🚀
<strong>The fast, Pythonic way to build MCP servers.</strong>
[](https://pypi.org/project/fastmcp)
[](https://github.com/jlowin/fastmcp/actions/workflows/run-tests.yml)
[](https://github.com/jlowin/fastmcp/blob/main/LICENSE)
</div>
[Model Context Protocol (MCP)](https://modelcontextprotocol.io) servers are a new, standardized way to provide context and tools to your LLMs, and FastMCP makes building MCP servers simple and intuitive. Create tools, expose resources, and define prompts with clean, Pythonic code:
```python
# demo.py
from fastmcp import FastMCP
mcp = FastMCP("Demo 🚀")
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
```
That's it! Give Claude access to the server by running:
```bash
fastmcp install demo.py
```
FastMCP handles all the complex protocol details and server management, so you can focus on building great tools. It's designed to be high-level and Pythonic - in most cases, decorating a function is all you need.
### Key features:
* **Fast**: High-level interface means less code and faster development
* **Simple**: Build MCP servers with minimal boilerplate
* **Pythonic**: Feels natural to Python developers
* **Complete***: FastMCP aims to provide a full implementation of the core MCP specification
(\*emphasis on *aims*)
🚨 🚧 🏗️ *FastMCP is under active development, as is the MCP specification itself. Core features are working but some advanced capabilities are still in progress.*
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## Table of Contents
- [Installation](#installation)
- [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 (Recommended for Building \& Testing)](#development-mode-recommended-for-building--testing)
- [Claude Desktop Integration (For Regular Use)](#claude-desktop-integration-for-regular-use)
- [Direct Execution (For Advanced Use Cases)](#direct-execution-for-advanced-use-cases)
- [Server Object Names](#server-object-names)
- [Examples](#examples)
- [Echo Server](#echo-server)
- [SQLite Explorer](#sqlite-explorer)
- [Contributing](#contributing)
- [Prerequisites](#prerequisites)
- [Installation](#installation-1)
- [Testing](#testing)
- [Formatting](#formatting)
- [Opening a Pull Request](#opening-a-pull-request)
## Installation
We strongly recommend installing FastMCP with [uv](https://docs.astral.sh/uv/), as it is required for deploying servers:
```bash
uv pip install fastmcp
```
Note: on macOS, uv may need to be installed with Homebrew (`brew install uv`) in order to make it available to the Claude Desktop app.
Alternatively, to use the SDK without deploying, you may use pip:
```bash
pip install fastmcp
```
## Quickstart
Let's create a simple MCP server that exposes a calculator tool and some data:
```python
# server.py
from 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
fastmcp install server.py
```
Alternatively, you can test it with the MCP Inspector:
```bash
fastmcp 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!
There is a low-level [Python SDK](https://github.com/modelcontextprotocol/python-sdk) available for implementing the protocol directly, but FastMCP aims to make that easier by providing a high-level, Pythonic interface.
## Core Concepts
### Server
The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:
```python
from fastmcp import FastMCP
# Create a named server
mcp = FastMCP("My App")
# Specify dependencies for deployment and development
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])
```
### 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. Some examples:
- File contents
- Database schemas
- API responses
- System information
Resources can be static:
```python
@mcp.resource("config://app")
def get_config() -> str:
"""Static configuration data"""
return "App configuration here"
```
Or dynamic with parameters (FastMCP automatically handles these as MCP templates):
```python
@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. They're similar to POST endpoints in a REST API.
Simple calculation example:
```python
@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)
```
HTTP request example:
```python
import httpx
@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
```
Complex input handling example:
```python
from pydantic import BaseModel, Field
from typing import Annotated
class ShrimpTank(BaseModel):
class Shrimp(BaseModel):
name: Annotated[str, Field(max_length=10)]
shrimp: list[Shrimp]
@mcp.tool()
def name_shrimp(
tank: ShrimpTank,
# You can use pydantic Field in function signatures for validation.
extra_names: Annotated[list[str], Field(max_length=10)],
) -> list[str]:
"""List all shrimp names in the tank"""
return [shrimp.name for shrimp in tank.shrimp] + extra_names
```
### Prompts
Prompts are reusable templates that help LLMs interact with your server effectively. They're like "best practices" encoded into your server. A prompt can be as simple as a string:
```python
@mcp.prompt()
def review_code(code: str) -> str:
return f"Please review this code:\n\n{code}"
```
Or a more structured sequence of messages:
```python
from fastmcp.prompts.base import UserMessage, AssistantMessage
@mcp.prompt()
def debug_error(error: str) -> list[Message]:
return [
UserMessage("I'm seeing this error:"),
UserMessage(error),
AssistantMessage("I'll help debug that. What have you tried so far?")
]
```
### Images
FastMCP provides an `Image` class that automatically handles image data in your server:
```python
from fastmcp import FastMCP, Image
from PIL import Image as PILImage
@mcp.tool()
def create_thumbnail(image_path: str) -> Image:
"""Create a thumbnail from an image"""
img = PILImage.open(image_path)
img.thumbnail((100, 100))
# FastMCP automatically handles conversion and MIME types
return Image(data=img.tobytes(), format="png")
@mcp.tool()
def load_image(path: str) -> Image:
"""Load an image from disk"""
# FastMCP handles reading and format detection
return Image(path=path)
```
Images can be used as the result of both tools and resources.
### Context
The Context object gives your tools and resources access to MCP capabilities. To use it, add a parameter annotated with `fastmcp.Context`:
```python
from fastmcp import FastMCP, Context
@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))
# Read another resource if needed
data = await ctx.read_resource(f"file://{file}")
return "Processing complete"
```
The Context object provides:
- Progress reporting through `report_progress()`
- Logging via `debug()`, `info()`, `warning()`, and `error()`
- Resource access through `read_resource()`
- Request metadata via `request_id` and `client_id`
## Running Your Server
There are three main ways to use your FastMCP server, each suited for different stages of development:
### Development Mode (Recommended for Building & Testing)
The fastest way to test and debug your server is with the MCP Inspector:
```bash
fastmcp dev server.py
```
This launches a web interface where you can:
- Test your tools and resources interactively
- See detailed logs and error messages
- Monitor server performance
- Set environment variables for testing
During development, you can:
- Add dependencies with `--with`:
```bash
fastmcp dev server.py --with pandas --with numpy
```
- Mount your local code for live updates:
```bash
fastmcp dev server.py --with-editable .
```
### Claude Desktop Integration (For Regular Use)
Once your server is ready, install it in Claude Desktop to use it with Claude:
```bash
fastmcp install server.py
```
Your server will run in an isolated environment with:
- Automatic installation of dependencies specified in your FastMCP instance:
```python
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])
```
- Custom naming via `--name`:
```bash
fastmcp install server.py --name "My Analytics Server"
```
- Environment variable management:
```bash
# Set variables individually
fastmcp install server.py -e API_KEY=abc123 -e DB_URL=postgres://...
# Or load from a .env file
fastmcp install server.py -f .env
```
### Direct Execution (For Advanced Use Cases)
For advanced scenarios like custom deployments or running without Claude, you can execute your server directly:
```python
from fastmcp import FastMCP
mcp = FastMCP("My App")
if __name__ == "__main__":
mcp.run()
```
Run it with:
```bash
# Using the FastMCP CLI
fastmcp run server.py
# Or with Python/uv directly
python server.py
uv run python server.py
```
Note: When running directly, you are responsible for ensuring all dependencies are available in your environment. Any dependencies specified on the FastMCP instance are ignored.
Choose this method when you need:
- Custom deployment configurations
- Integration with other services
- Direct control over the server lifecycle
### Server Object Names
All FastMCP commands will look for a server object called `mcp`, `app`, or `server` in your file. If you have a different object name or multiple servers in one file, use the syntax `server.py:my_server`:
```bash
# Using a standard name
fastmcp run server.py
# Using a custom name
fastmcp run server.py:my_custom_server
```
## Examples
Here are a few examples of FastMCP servers. For more, see the `examples/` directory.
### Echo Server
A simple server demonstrating resources, tools, and prompts:
```python
from 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
from fastmcp import FastMCP
import sqlite3
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)}"
@mcp.prompt()
def analyze_table(table: str) -> str:
"""Create a prompt template for analyzing tables"""
return f"""Please analyze this database table:
Table: {table}
Schema:
{get_schema()}
What insights can you provide about the structure and relationships?"""
```
## Contributing
<details>
<summary><h3>Open Developer Guide</h3></summary>
### Prerequisites
FastMCP requires Python 3.10+ and [uv](https://docs.astral.sh/uv/).
### Installation
For development, we recommend installing FastMCP with development dependencies, which includes various utilities the maintainers find useful.
```bash
git clone https://github.com/jlowin/fastmcp.git
cd fastmcp
uv sync --frozen --extra dev
```
For running tests only (e.g., in CI), you only need the testing dependencies:
```bash
uv sync --frozen --extra tests
```
### Testing
Please make sure to test any new functionality. Your tests should be simple and atomic and anticipate change rather than cement complex patterns.
Run tests from the root directory:
```bash
pytest -vv
```
### Formatting
FastMCP enforces a variety of required formats, which you can automatically enforce with pre-commit.
Install the pre-commit hooks:
```bash
pre-commit install
```
The hooks will now run on every commit (as well as on every PR). To run them manually:
```bash
pre-commit run --all-files
```
### Opening a Pull Request
Fork the repository and create a new branch:
```bash
git checkout -b my-branch
```
Make your changes and commit them:
```bash
git add . && git commit -m "My changes"
```
Push your changes to your fork:
```bash
git push origin my-branch
```
Feel free to reach out in a GitHub issue or discussion if you have any questions!
</details>
