MCP Server Creator

Production MCP Server Builder

Build production-ready Model Context Protocol (MCP) servers using the official MCP SDK with security, monitoring, and and deployment best practices.

Quick Start

Install the Official MCP SDK

# Using pip
pip install mcp

# Using uv
uv add mcp

Create New MCP Server

# server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP(
    name="My Production Server",
    json_response=True,
    host="127.0.0.1",
    port=8080
)

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"

@mcp.prompt()
def greet_user(name: str, style: str = "friendly") -> str:
    """Generate a greeting prompt"""
    return f"Write a {style} greeting for someone named {name}."

if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Run the Server

# Run with uv
uv run --with mcp server.py

# Or with pip
python server.py

Connect with MCP Inspector

npx -y @modelcontextprotocol/inspector

Architecture Overview

Production MCP servers follow these key principles:

1. Transport Layer: Use streamable-http for production (vs stdio for development)
2. Authentication: Implement OAuth or API key validation
3. Security: Apply input validation, rate limiting, and access controls
4. Monitoring: Include health checks, metrics, and logging
5. Reliability: Implement graceful error handling and shutdown

Core Concepts

The MCP SDK provides three core primitives:

1. Tools: Functions that can be called remotely
2. Resources: Named pieces of data that can be retrieved
3. Prompts: Templates for generating prompts for LLMs

Development Workflow

1. Install: Install the official mcp package
2. Create: Initialize a FastMCP server instance
3. Define: Add tools, resources, and prompts using decorators
4. Run: Start the server with appropriate transport
5. Test: Verify functionality with MCP Inspector
6. Deploy: Push to production with monitoring

Production Best Practices

Security

• Use HTTPS in production
• Implement proper authentication
• Validate all inputs
• Sanitize all outputs
• Apply rate limiting

Reliability

• Implement health checks (/health endpoint)
• Add graceful shutdown handling
• Include comprehensive error handling
• Use structured logging

Monitoring

• Expose metrics endpoint
• Log key operations
• Monitor performance
• Set up alerts

Tool Definition Patterns

Basic Tool

@mcp.tool()
def my_tool(param1: str, param2: int) -> dict:
    """Description of what this tool does."""
    # Tool implementation
    return {"result": "success"}

Resource Definition

@mcp.resource("my-resource://{identifier}")
def get_resource(identifier: str) -> str:
    """Define a retrievable resource."""
    return f"Resource content for {identifier}"

Prompt Definition

@mcp.prompt()
def my_prompt(context: str, style: str = "professional") -> str:
    """Define a prompt template."""
    return f"Write a {style} response about: {context}"

Tool with Error Handling

import logging
logger = logging.getLogger(__name__)

@mcp.tool()
def secure_tool(input_data: str) -> str:
    """Tool with proper error handling and validation."""
    try:
        # Validate input
        if len(input_data) > MAX_INPUT_LENGTH:
            raise ValueError("Input too long")

        # Process data
        result = process_input(input_data)
        return result

    except ValueError as e:
        # Log error
        logger.error(f"Validation error: {e}")
        raise
    except Exception as e:
        # Log unexpected errors
        logger.error(f"Unexpected error: {e}")
        raise RuntimeError("An error occurred processing your request")

Server Configuration

Transport Options

• stdio: Standard input/output, typically for Claude integration
• streamable-http: HTTP-based transport, recommended for production
• sse: Server-sent events (alternative for streaming)

Running with Different Transports

# For production
mcp.run(transport="streamable-http")

# For development
mcp.run(transport="stdio")

Validation

Validate your server implementation:

• Use MCP Inspector to test connectivity
• Verify all tools respond correctly
• Check resource and prompt definitions
• Test error handling paths

Deployment

Environment Variables

# For production configuration
MCP_SERVER_NAME=my-server
MCP_SERVER_PORT=8000

Monitoring and Health Checks

Health Endpoint

Implement a health check endpoint in your application:

from starlette.responses import JSONResponse
from starlette.requests import Request
from datetime import datetime

# Add custom endpoints to your FastMCP server
@mcp.custom_route("/health", methods=["GET"])
async def health_check(request: Request):
    return JSONResponse({
        "status": "healthy",
        "timestamp": datetime.now().isoformat(),
        "service": "my-mcp-server",
        "version": "1.0.0"
    })

# You can also add other custom endpoints

Custom Routes

The custom_route decorator allows you to add additional HTTP endpoints to your MCP server beyond the standard MCP protocol endpoints. This is useful for:

• Health checks (/health, /status)
• Metrics (/metrics, /stats)

The handler function must accept a Starlette Request parameter and return a Response.

Troubleshooting

References

For detailed information on specific topics, see:

• resources/tools-usage.md - Tools usage guide
• resources/prompts-usage.md - Prompts usage guide
• resources/resources-usage.md - Resources usage guide
• resources/fastmcp-arguments.md - FastMCP constructor arguments documentation
• references/testing-guide.md - Testing guide for MCP servers
• references/mcp-architecture.md - Architecture patterns
• references/monitoring-guide.md - Monitoring and observability
• references/security-best-practices.md - Security guidelines
• references/tool-definition-patterns.md - Tool implementation patterns

Source: syeda-hoorain-ali/claude-code-skills — distributed by TomeVault.