The MCP Protocol: Connecting AI to Your Tools
The MCP Protocol: Connecting AI to Your Tools
1. What Is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how AI applications connect to external tools, data sources, and services. Think of it as USB-C for AI — a universal interface that lets any LLM interact with any tool through a common protocol.
2. Why MCP Matters
Before MCP, every AI tool integration required custom code. Each model provider had its own function calling format, each tool had its own API, and developers spent more time on plumbing than on features. MCP provides a standard interface where:
- AI hosts (like Claude Desktop or custom apps) discover available tools
- Tools advertise their capabilities through a standard schema
- The host handles authentication, rate limiting, and error handling
3. MCP Architecture
MCP follows a client-server architecture. The AI host acts as the MCP client, connecting to MCP servers that expose tools and resources.
1import { Server } from "@modelcontextprotocol/sdk/server";
2import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";
3
4const server = new Server(
5{ name: "github-tools", version: "1.0.0" },
6{ capabilities: { tools: {} } },
7);
8
9server.setRequestHandler("tools/list", async () => ({
10tools: [{
11 name: "search_repos",
12 description: "Search GitHub repositories",
13 inputSchema: {
14 type: "object",
15 properties: {
16 query: { type: "string" },
17 limit: { type: "number", default: 10 },
18 },
19 },
20}],
21}));
22
23server.setRequestHandler("tools/call", async (request) => {
24if (request.params.name === "search_repos") {
25 // Call GitHub API and return results
26 return { content: [{ type: "text", text: "Results..." }] };
27}
28});
29
30const transport = new StdioServerTransport();
31await server.connect(transport);4. Building MCP Tools
MCP servers can be built in any language. The protocol uses JSON-RPC over stdin/stdout or WebSocket. This means you can expose any existing API, database, or service as an MCP-compatible tool.
1from mcp.server import Server
2from mcp.server.stdio import stdio_server
3
4app = Server("database-query")
5
6@app.list_tools()
7async def list_tools():
8 return [
9 {
10 "name": "query_database",
11 "description": "Run SQL queries",
12 "inputSchema": {
13 "type": "object",
14 "properties": {
15 "sql": {"type": "string"}
16 }
17 }
18 }
19 ]
20
21@app.call_tool()
22async def call_tool(name: str, arguments: dict):
23 if name == "query_database":
24 result = run_query(arguments["sql"])
25 return [{"type": "text", "text": str(result)}]
26
27if __name__ == "__main__":
28 with stdio_server() as (read, write):
29 app.run(read, write)5. Use Cases
- AI coding assistants that can query databases, search documentation, and manage deployments
- Agent workflows that interact with GitHub, Jira, Slack, and other tools
- Custom AI applications that need access to internal APIs and data sources
6. Verdict
MCP is still evolving, but it represents a fundamental shift in how AI applications integrate with the world. Building your tools as MCP servers future-proofs them against changes in AI models and frameworks. As more hosts adopt MCP, the value of the protocol grows exponentially.