Build Your Own MCP Server — Step-by-Step Guide

Architecture Overview

Claude Code (MCP Client)
    ↓ JSON-RPC over stdio/SSE
Your MCP Server
    ↓ Your code
External Service (API, DB, SaaS)

Three Primitives

TypeScript Implementation

1. Project Setup

npx @anthropic/create-mcp-server my-server
cd my-server

2. Define Tools

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({ name: "my-server", version: "1.0.0" }, {
  capabilities: { tools: {} }
});

server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "create_ticket",
    description: "Create a support ticket in our system",
    inputSchema: {
      type: "object",
      properties: {
        title: { type: "string", description: "Ticket title" },
        priority: { type: "string", enum: ["low", "medium", "high"] }
      },
      required: ["title"]
    }
  }]
}));

server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "create_ticket") {
    const { title, priority } = request.params.arguments;
    // Call your API here
    const ticket = await createTicket(title, priority);
    return { content: [{ type: "text", text: `Created ticket #${ticket.id}` }] };
  }
});

const transport = new StdioServerTransport();
await server.connect(transport);

3. Define Resources

server.setRequestHandler("resources/list", async () => ({
  resources: [{
    uri: "config://app-settings",
    name: "Application Settings",
    mimeType: "application/json"
  }]
}));

server.setRequestHandler("resources/read", async (request) => {
  if (request.params.uri === "config://app-settings") {
    return { contents: [{ uri: request.params.uri, text: JSON.stringify(settings) }] };
  }
});

Python Implementation

from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("my-server")

@server.list_tools()
async def list_tools():
    return [Tool(
        name="search_docs",
        description="Search internal documentation",
        inputSchema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}
    )]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "search_docs":
        results = search(arguments["query"])
        return [TextContent(type="text", text=str(results))]

if __name__ == "__main__":
    import asyncio
    from mcp.server.stdio import stdio_server
    asyncio.run(stdio_server(server))

Testing

Add to .mcp.json:

{
  "mcpServers": {
    "my-server": {
      "command": "node",
      "args": ["./dist/index.js"]
    }
  }
}

Restart Claude Code and test: "Use my-server to create a ticket titled 'Fix login bug'"

Deployment Patterns

FAQ

Q: What is an MCP server? A: A program that exposes tools, resources, and prompts to AI agents via the Model Context Protocol — letting agents interact with external services.

Q: Which language should I use? A: TypeScript for web APIs and npm distribution. Python for data science and ML tools.

Q: How do I publish my MCP server? A: Publish to npm (npx my-mcp-server) or PyPI (pip install my-mcp-server). Add to the MCP servers registry.