MCP Python SDK
which python
python3 -m venv myenv
source myenv/Scripts/activate
pip install -r requirements.txt
python fast_mcp_server.py
Usage
to run the client
to run the server
uvicorn ne04j_mcp_server:app --host 0.0.0.0 --port 8000
Data Prep
Insert Node:
CREATE (p1:Person {name: "Tom Hanks", birthYear: 1956})
CREATE (p2:Person {name: "Kevin Bacon", birthYear: 1958})
CREATE (m1:Movie {title: "Forrest Gump", releaseYear: 1994})
CREATE (m2:Movie {title: "Apollo 13", releaseYear: 1995})
Insert Relationship
MATCH (p:Person {name: "Tom Hanks"}), (m:Movie {title: "Forrest Gump"})
CREATE (p)-[:ACTED_IN]->(m)
MATCH (p:Person {name: "Tom Hanks"}), (m:Movie {title: "Apollo 13"})
CREATE (p)-[:ACTED_IN]->(m)
MATCH (p1:Person {name: "Tom Hanks"}), (p2:Person {name: "Kevin Bacon"})
CREATE (p1)-[:FRIENDS_WITH]->(p2)
Insert properties
MATCH (p:Person {name: "Tom Hanks"})
SET p.oscarsWon = 2
MATCH (m:Movie {title: "Forrest Gump"})
SET m.genre = "Drama"
MATCH (p:Person {name: "Tom Hanks"})-[r:ACTED_IN]->(m:Movie {title: "Forrest Gump"})
SET r.role = "Forrest Gump"
Insert Complex Structure
// Create a community and then match persons to add them to the community
CREATE (c1:Community {name: "Hollywood Stars"})
WITH c1
MATCH (p:Person)
WHERE p.name IN ["Tom Hanks", "Kevin Bacon"]
CREATE (p)-[:MEMBER_OF]->(c1)
other things - boiler plate - no clue - ignore for now.
Python implementation of the Model Context Protocol (MCP)
Table of Contents
Overview
The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:
- Build MCP clients that can connect to any MCP server
- Create MCP servers that expose resources, prompts and tools
- Use standard transports like stdio and SSE
- Handle all MCP protocol messages and lifecycle events
Installation
We recommend using uv to manage your Python projects:
Alternatively:
Quickstart
Let's create a simple MCP server that exposes a calculator tool and some data:
# server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("Demo")
# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
"""Get a personalized greeting"""
return f"Hello, {name}!"
You can install this server in Claude Desktop and interact with it right away by running:
Alternatively, you can test it with the MCP Inspector:
What is MCP?
The Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions. MCP servers can:
- Expose data through Resources (think of these sort of like GET endpoints; they are used to load information into the LLM's context)
- Provide functionality through Tools (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)
- Define interaction patterns through Prompts (reusable templates for LLM interactions)
- And more!
Core Concepts
Server
The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:
# Add lifespan support for startup/shutdown with strong typing
from dataclasses import dataclass
from typing import AsyncIterator
from mcp.server.fastmcp import FastMCP
# Create a named server
mcp = FastMCP("My App")
# Specify dependencies for deployment and development
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])
@dataclass
class AppContext:
db: Database # Replace with your actual DB type
@asynccontextmanager
async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:
"""Manage application lifecycle with type-safe context"""
try:
# Initialize on startup
await db.connect()
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:
@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 let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:
@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:
@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[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:
from mcp.server.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))
return Image(data=img.tobytes(), format="png")
Context
The Context object gives your tools and resources access to MCP capabilities:
from mcp.server.fastmcp import FastMCP, Context
@mcp.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:
mcp dev server.py
# Add dependencies
mcp dev server.py --with pandas --with numpy
# Mount local code
mcp dev server.py --with-editable .
Claude Desktop Integration
Once your server is ready, install it in Claude Desktop:
mcp install server.py
# Custom name
mcp install server.py --name "My Analytics Server"
# Environment variables
mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
mcp install server.py -f .env
Direct Execution
For advanced scenarios like custom deployments:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
if __name__ == "__main__":
mcp.run()
Run it with:
python server.py
# or
mcp run server.py
Examples
Echo Server
A simple server demonstrating resources, tools, and prompts:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Echo")
@mcp.resource("echo://{message}")
def echo_resource(message: str) -> str:
"""Echo a message as a resource"""
return f"Resource echo: {message}"
@mcp.tool()
def echo_tool(message: str) -> str:
"""Echo a message as a tool"""
return f"Tool echo: {message}"
@mcp.prompt()
def echo_prompt(message: str) -> str:
"""Create an echo prompt"""
return f"Please process this message: {message}"
SQLite Explorer
A more complex example showing database integration:
from mcp.server.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)}"
Advanced Usage
Low-Level Server
For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API:
from contextlib import asynccontextmanager
from typing import AsyncIterator
@asynccontextmanager
async def server_lifespan(server: Server) -> AsyncIterator[dict]:
"""Manage server startup and shutdown lifecycle."""
try:
# Initialize resources on startup
await db.connect()
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
from mcp.server.lowlevel import Server, NotificationOptions
from mcp.server.models import InitializationOptions
import mcp.server.stdio
import mcp.types as types
# 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:
from mcp import ClientSession, StdioServerParameters
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 | subscribelistChanged | Resource exposure and updates |
tools | listChanged | Tool discovery and execution |
logging | - | Server logging configuration |
completion | - | Argument completion suggestions |
Documentation
Contributing
We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the contributing guide to get started.
License
This project is licensed under the MIT License - see the LICENSE file for details.