MCP (Model Context Protocol) is the standard that lets Claude — and any MCP-compatible AI — talk directly to your APIs. Here's how to make your API Claude-compatible in 10 minutes.
What MCP Actually Does
Without MCP: Claude can read text about your API and suggest curl commands.
With MCP: Claude can call your API directly, read the response, and take action.
The difference is huge for automation workflows.
The Minimal MCP Server
# mcp_server.py
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import requests
server = Server("my-api-mcp")
@server.list_tools()
async def list_tools():
return [
Tool(
name="calculate_ai_cost",
description="Calculate the cost of an AI API call",
inputSchema={
"type": "object",
"properties": {
"model": {"type": "string"},
"input_tokens": {"type": "integer"},
"output_tokens": {"type": "integer"}
},
"required": ["model", "input_tokens", "output_tokens"]
}
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict):
if name == "calculate_ai_cost":
resp = requests.get("https://api.lazy-mac.com/ai-spend/calculate", params=arguments)
return [TextContent(type="text", text=str(resp.json()))]
async def main():
async with stdio_server() as (read, write):
await server.run(read, write, server.create_initialization_options())
if __name__ == "__main__":
import asyncio
asyncio.run(main())
Wire it to Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"my-api": {
"command": "python3",
"args": ["/path/to/mcp_server.py"]
}
}
}
Restart Claude Desktop. Now you can say: "Calculate the cost of 10,000 GPT-4o calls with 500 input and 200 output tokens each" — and Claude just does it.
Why This Matters for Revenue
MCP-compatible APIs are listed in directories like Smithery and MCPize. That's additional discovery surface on top of RapidAPI and Gumroad.
I have 24 APIs registered on MCPize. Zero additional code — just register the endpoint URL.
