Python MCP Server Development
Instructions
- Use uv for project management:
uv init mcp-server-demo and uv add "mcp[cli]"
- Import FastMCP from
mcp.server.fastmcp: from mcp.server.fastmcp import FastMCP
- Use
@mcp.tool(), @mcp.resource(), and @mcp.prompt() decorators for registration
- Type hints are mandatory - they're used for schema generation and validation
- Use Pydantic models, TypedDicts, or dataclasses for structured output
- Tools automatically return structured output when return types are compatible
- For stdio transport, use
mcp.run() or mcp.run(transport="stdio")
- For HTTP servers, use
mcp.run(transport="streamable-http") or mount to Starlette/FastAPI
- Use
Context parameter in tools/resources to access MCP capabilities: ctx: Context
- Send logs with
await ctx.debug(), await ctx.info(), await ctx.warning(), await ctx.error()
- Report progress with
await ctx.report_progress(progress, total, message)
- Request user input with
await ctx.elicit(message, schema)
- Use LLM sampling with
await ctx.session.create_message(messages, max_tokens)
- Configure icons with
Icon(src="path", mimeType="image/png") for server, tools, resources, prompts
- Use
Image class for automatic image handling: return Image(data=bytes, format="png")
- Define resource templates with URI patterns:
@mcp.resource("greeting://{name}")
- Implement completion support by accepting partial values and returning suggestions
- Use lifespan context managers for startup/shutdown with shared resources
- Access lifespan context in tools via
ctx.request_context.lifespan_context
- For stateless HTTP servers, set
stateless_http=True in FastMCP initialization
- Enable JSON responses for modern clients:
json_response=True
- Test servers with:
uv run mcp dev server.py (Inspector) or uv run mcp install server.py (Claude Desktop)
- Mount multiple servers in Starlette with different paths:
Mount("/path", mcp.streamable_http_app())
- Configure CORS for browser clients: expose
Mcp-Session-Id header
- Use low-level Server class for maximum control when FastMCP isn't sufficient
Best Practices
- Always use type hints - they drive schema generation and validation
- Return Pydantic models or TypedDicts for structured tool outputs
- Keep tool functions focused on single responsibilities
- Provide clear docstrings - they become tool descriptions
- Use descriptive parameter names with type hints
- Validate inputs using Pydantic Field descriptions
- Implement proper error handling with try-except blocks
- Use async functions for I/O-bound operations
- Clean up resources in lifespan context managers
- Log to stderr to avoid interfering with stdio transport (when using stdio)
- Use environment variables for configuration
- Test tools independently before LLM integration
- Consider security when exposing file system or network access
- Use structured output for machine-readable data
- Provide both content and structured data for backward compatibility
Common Patterns
Basic Server Setup (stdio)
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My Server")
@mcp.tool()
def calculate(a: int, b: int, op: str) -> int:
"""Perform calculation"""
if op == "add":
return a + b
return a - b
if __name__ == "__main__":
mcp.run() # stdio by default
HTTP Server
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My HTTP Server")
@mcp.tool()
def hello(name: str = "World") -> str:
"""Greet someone"""
return f"Hello, {name}!"
if __name__ == "__main__":
mcp.run(transport="streamable-http")
Tool with Structured Output
from pydantic import BaseModel, Field
class WeatherData(BaseModel):
temperature: float = Field(description="Temperature in Celsius")
condition: str
humidity: float
@mcp.tool()
def get_weather(city: str) -> WeatherData:
"""Get weather for a city"""
return WeatherData(
temperature=22.5,
condition="sunny",
humidity=65.0
)
Dynamic Resource
@mcp.resource("users://{user_id}")
def get_user(user_id: str) -> str:
"""Get user profile data"""
return f"User {user_id} profile data"
Tool with Context
from mcp.server.fastmcp import Context
from mcp.server.session import ServerSession
@mcp.tool()
async def process_data(
data: str,
ctx: Context[ServerSession, None]
) -> str:
"""Process data with logging"""
await ctx.info(f"Processing: {data}")
await ctx.report_progress(0.5, 1.0, "Halfway done")
return f"Processed: {data}"
Tool with Sampling
from mcp.server.fastmcp import Context
from mcp.server.session import ServerSession
from mcp.types import SamplingMessage, TextContent
@mcp.tool()
async def summarize(
text: str,
ctx: Context[ServerSession, None]
) -> str:
"""Summarize text using LLM"""
result = await ctx.session.create_message(
messages=[SamplingMessage(
role="user",
content=TextContent(type="text", text=f"Summarize: {text}")
)],
max_tokens=100
)
return result.content.text if result.content.type == "text" else ""
Lifespan Management
from contextlib import asynccontextmanager
from dataclasses import dataclass
from mcp.server.fastmcp import FastMCP, Context
@dataclass
class AppContext:
db: Database
@asynccontextmanager
async def app_lifespan(server: FastMCP):
db = await Database.connect()
try:
yield AppContext(db=db)
finally:
await db.disconnect()
mcp = FastMCP("My App", lifespan=app_lifespan)
@mcp.tool()
def query(sql: str, ctx: Context) -> str:
"""Query database"""
db = ctx.request_context.lifespan_context.db
return db.execute(sql)
Prompt with Messages
from mcp.server.fastmcp.prompts import base
@mcp.prompt(title="Code Review")
def review_code(code: str) -> list[base.Message]:
"""Create code review prompt"""
return [
base.UserMessage("Review this code:"),
base.UserMessage(code),
base.AssistantMessage("I'll review the code for you.")
]
Error Handling
@mcp.tool()
async def risky_operation(input: str) -> str:
"""Operation that might fail"""
try:
result = await perform_operation(input)
return f"Success: {result}"
except Exception as e:
return f"Error: {str(e)}"
pingqLIN