Getting Started: Deploying Your First Production MCP Server

The AI development landscape is evolving rapidly, and one of the most exciting developments is Anthropic’s Model Context Protocol (MCP). If you've ever been frustrated by the constant context switching between AI models and the loss of conversation history when moving between different AI clients, MCP might be the solution you've been looking for.

This quickstart guide gives you clear instructions on how to build your first MCP server, along with a simple template that you can follow. For more in-depth instructions, check out our full guide.

Step-by-Step Development Process

1. Identify the Need: Start with a tool you use frequently that could benefit from AI integration

2. Design the Interface: Plan your tools, resources, and prompts before coding

3. Build Incrementally: Start with one tool, test it, then add more

4. Add Error Handling: Make your server robust with proper error handling

5. Optimize Performance: Add caching and async operations where beneficial

6. Deploy and Iterate: Connect to your AI client and refine based on usage

Common Beginner Mistakes to Avoid

• Overcomplicating: Start simple and add complexity gradually

• Poor Error Handling: Always handle external API failures gracefully

• Ignoring Security: Never hardcode API keys or sensitive data

• Skipping Testing: Always test with MCP Inspector before connecting to AI clients

• Unclear Documentation: Write clear tool descriptions and prompts

Quick Start Template

Here's a template to get you started quickly:

from mcp.server.fastmcp import FastMCP
import os
import httpx
import json
# Initialize server
mcp = FastMCP("My Custom Server")
# Load configuration
API_KEY = os.getenv("MY_API_KEY")
if not API_KEY:
    raise ValueError("MY_API_KEY environment variable required")
@mcp.tool()
async def my_first_tool(input_data: str) -> str:
    """Description of what this tool does"""
    try:
        # Your logic here
        return f"Processed: {input_data}"
    except Exception as e:
        return f"Error: {str(e)}"
@mcp.resource("data://my-resource")
async def my_resource() -> str:
    """Description of this resource"""
    return json.dumps({"status": "active", "data": "sample"})
@mcp.prompt()
def usage_guidance() -> str:
    """How to use this server effectively"""
    return """
    This server provides tools for [your use case].
    Use my_first_tool to [specific functionality].
    Check my_resource for [what it provides].
    """
if __name__ == "__main__":