mcp-builder（MCP 构建器）

MCP Builder

Build a working MCP server from a description of the tools you need. Produces a deployable Python server using FastMCP.

Workflow

Step 1: Define What to Expose

Ask what the server needs to provide:

• Tools -- Functions Claude can call (API wrappers, calculations, file operations)
• Resources -- Data Claude can read (database records, config, documents)
• Prompts -- Reusable prompt templates with parameters

A brief like "MCP server for querying our customer database" is enough.

Step 2: Scaffold the Server

pip install fastmcp

Create the server file. The server instance MUST be at module level:

from fastmcp import FastMCP

# MUST be at module level for FastMCP Cloud
mcp = FastMCP("My Server")

@mcp.tool()
async def search_customers(query: str) -> str:
    """Search customers by name or email."""
    # Implementation here
    return f"Found customers matching: {query}"

@mcp.resource("customers://{customer_id}")
async def get_customer(customer_id: str) -> str:
    """Get customer details by ID."""
    return f"Customer {customer_id} details"

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

Step 3: Add Companion CLI Scripts (Optional)

For Claude Code terminal use, add scripts alongside the MCP server:

my-mcp-server/
├── src/index.ts          # MCP server (for Claude.ai)
├── scripts/
│   ├── search.ts         # CLI version of search tool
│   └── _shared.ts        # Shared auth/config
├── SCRIPTS.md            # Documents available scripts
└── package.json

CLI scripts provide file I/O, batch processing, and richer output that MCP can't. See assets/SCRIPTS-TEMPLATE.md and assets/script-template.ts for TypeScript templates.

Step 4: Test Locally

Quick test -- run directly:

python server.py

Dev mode with inspector UI (recommended):

fastmcp dev server.py
# Opens inspector at http://localhost:5173
# Hot reload, detailed logging, tool/resource inspection

HTTP mode for remote clients:

python server.py --transport http --port 8000

Automated test script using FastMCP Client:

import asyncio
from fastmcp import Client

async def test_server(server_path):
    async with Client(server_path) as client:
        # List everything
        tools = await client.list_tools()
        resources = await client.list_resources()
        prompts = await client.list_prompts()

        print(f"Tools: {[t.name for t in tools]}")
        print(f"Resources: {[r.uri for r in resources]}")
        print(f"Prompts: {[p.name for p in prompts]}")

        # Call first tool
        if tools:
            result = await client.call_tool(tools[0].name, {})
            print(f"Tool result: {result}")

        # Read first resource
        if resources:
            data = await client.read_resource(resources[0].uri)
            print(f"Resource data: {data}")

asyncio.run(test_server("server.py"))

Step 5: Pre-Deploy Checklist

Run these checks before deploying. All required checks must pass.

Required (will cause deploy failure):

1. Server file exists
2. Python syntax valid: python3 -m py_compile server.py
3. Module-level server object (not inside a function): grep -q "^mcp = FastMCP\|^server = FastMCP\|^app = FastMCP" server.py
4. requirements.txt exists with PyPI packages only (no git+, -e, .whl, .tar.gz)
5. No hardcoded secrets (check for api_key = "..." patterns excluding os.getenv/os.environ)

Advisory (warnings):

1. fastmcp listed in requirements.txt
2. .gitignore includes .env
3. No circular imports
4. Git repository initialised with remote
5. Server can load: timeout 5 fastmcp inspect server.py

Step 6: Deploy

FastMCP Cloud (simplest):

git add . && git commit -m "Ready for deployment"
git push -u origin main
# Visit https://fastmcp.cloud, connect repo, add env vars, deploy
# URL: https://your-project.fastmcp.app/mcp

Cloud requirements:

• Module-level server object named mcp, server, or app
• PyPI dependencies only in requirements.txt
• Public GitHub repository
• Environment variables for secrets (no hardcoded values)
• Auto-deploys on push to main, PR preview deployments

Docker (self-hosted):

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["python", "server.py", "--transport", "http", "--port", "8000"]

Cloudflare Workers (edge): See the cloudflare-worker-builder skill for Workers-based MCP servers.

Critical Patterns

Module-Level Server Instance

FastMCP Cloud requires the server instance at module level:

# CORRECT
mcp = FastMCP("My Server")

@mcp.tool()
def my_tool(): ...

# WRONG -- Cloud can't find the server
def create_server():
    mcp = FastMCP("My Server")
    return mcp

# FIX for factory pattern -- export at module level
def create_server() -> FastMCP:
    mcp = FastMCP("server")
    return mcp
mcp = create_server()

Type Annotations Required

FastMCP uses type annotations to generate tool schemas:

@mcp.tool()
async def search(
    query: str,           # Required parameter
    limit: int = 10,      # Optional with default
    tags: list[str] = []  # Complex types supported
) -> str:
    """Docstring becomes the tool description."""
    ...

Error Handling

Return errors as strings, don't raise exceptions:

@mcp.tool()
async def get_data(id: str) -> str:
    try:
        result = await fetch_data(id)
        return json.dumps(result)
    except NotFoundError:
        return f"Error: No data found for ID {id}"

Cloud-Ready Server Pattern

import os
from fastmcp import FastMCP

mcp = FastMCP("production-server")
API_KEY = os.getenv("API_KEY")

@mcp.tool()
async def production_tool(data: str) -> dict:
    if not API_KEY:
        return {"error": "API_KEY not configured"}
    return {"status": "success", "data": data}

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

Common Errors and Fixes

These are the errors you will hit. Fix them before deploying.

Production Patterns

Self-Contained Server

Keep all utilities in one file to avoid circular imports:

from fastmcp import FastMCP
import os

mcp = FastMCP("my-server")

# Config
class Config:
    API_KEY = os.getenv("API_KEY", "")
    BASE_URL = os.getenv("BASE_URL", "https://api.example.com")

# Helpers
def format_success(data): return {"status": "success", "data": data}
def format_error(msg): return {"status": "error", "message": msg}

@mcp.tool()
async def my_tool(query: str) -> dict:
    if not Config.API_KEY:
        return format_error("API_KEY not configured")
    return format_success({"query": query})

Lazy Initialisation

Don't create async resources at module level. Initialise on first use:

_db = None

async def get_db():
    global _db
    if _db is None:
        _db = await create_connection(Config.DB_URL)
    return _db

Health Check Resource

@mcp.resource("health://status")
async def health_check() -> dict:
    return {
        "status": "healthy",
        "version": "1.0.0",
        "checks": {
            "api": "connected",
            "database": "connected"
        }
    }

Connection Pooling

import httpx

_client = None

def get_client() -> httpx.AsyncClient:
    global _client
    if _client is None:
        _client = httpx.AsyncClient(
            base_url=Config.BASE_URL,
            headers={"Authorization": f"Bearer {Config.API_KEY}"},
            limits=httpx.Limits(max_connections=20, max_keepalive_connections=5),
            timeout=30.0
        )
    return _client

Retry with Backoff

async def retry_with_backoff(func, max_retries=3, initial_delay=1.0):
    for attempt in range(max_retries):
        try:
            return await func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            delay = initial_delay * (2 ** attempt)
            await asyncio.sleep(delay)

Context Features (Advanced)

Context Injection

from fastmcp import Context

@mcp.tool()
async def tool_with_context(param: str, context: Context) -> dict:
    # Context parameter MUST have type hint
    pass

Progress Tracking

@mcp.tool()
async def long_task(items: list[str], context: Context) -> str:
    for i, item in enumerate(items):
        await context.report_progress(i + 1, len(items), f"Processing {item}")
        await process(item)
    return "Done"

Sampling (LLM from within tools)

@mcp.tool()
async def summarise(text: str, context: Context) -> str:
    result = await context.request_sampling(
        messages=[{"role": "user", "content": f"Summarise: {text}"}],
        max_tokens=200
    )
    return result

CLI Quick Reference

fastmcp dev server.py              # Dev mode with inspector UI
fastmcp run server.py              # Run (stdio)
fastmcp run server.py --transport http --port 8000  # Run (HTTP)
fastmcp inspect server.py          # Inspect without running
fastmcp install server.py          # Install to Claude Desktop
fastmcp deploy server.py --name my-server  # Deploy to Cloud

Environment variables: FASTMCP_LOG_LEVEL (DEBUG/INFO/WARNING/ERROR), FASTMCP_ENV (development/staging/production).

Integration Patterns (Optional)

For specific integration approaches, see references/integration-patterns.md:

• Manual API -- httpx.AsyncClient with reusable client
• OpenAPI auto-generation -- FastMCP.from_openapi(spec, client, route_maps=[...])
• FastAPI conversion -- FastMCP.from_fastapi(app)

Asset Files

• assets/basic-server.py -- Minimal FastMCP server template
• assets/self-contained-server.py -- Server with storage and middleware
• assets/tools-examples.py -- Tool patterns and type annotations
• assets/resources-examples.py -- Resource URI patterns
• assets/prompts-examples.py -- Prompt template patterns
• assets/client-example.py -- MCP client usage
• assets/SCRIPTS-TEMPLATE.md -- CLI companion docs template
• assets/script-template.ts -- TypeScript CLI script template