Agentic AI Notebook
Model Context Protocol
Phase 8Module 23 of 25

Build MCP Server

Once you can ship a tiny server, any internal system — tickets, metrics, deploys — can show up as tools in every MCP client.

Building an MCP server is writing an API that every AI app already knows how to call.

Visual Workflows

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Overview

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Do not skip Inspector

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If Inspector fails, Cursor will fail too — with a worse error.

Key Takeaways

  • 1.Pick an SDK: Python mcp or TypeScript @modelcontextprotocol/sdk. Define 1–3 tools with tight schemas, then implement call_tool.
  • 2.Run locally on stdio first; test with MCP Inspector before any host. Only then add the server to mcp.json and try it inside Cursor.
  • 3.Start with one tool. A server that greets you is enough to prove initialize, schemas, and stdio.
  • 4.Add real APIs only after Inspector is green.

Learn elsewhere

  • Wiring discovered tools into an agent loop — last module
  • Remote deploy — Remote MCP

Real Example

Scenario

You wrap two Datadog tools — query_metrics and list_alerts — test them in Inspector, then every team agent can watch production.

What you would do

In Model Context Protocol, apply Build MCP Server to this scenario: You wrap two Datadog tools — query_metrics and list_alerts — test them in Inspector, then every team agent can watch production. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Commands

Commands to Remember

  • pip install mcp
  • npx @modelcontextprotocol/inspector
  • stdio_server() for local first
  • Add to mcp.json only after Inspector passes

Cheat Sheet

Quick recap

quick ref
  • SDK → tools → Inspector → mcp.json
  • One tool is a complete first server
  • stdout stays JSON-RPC only
  • HTTP and auth come after local works

Common Mistakes

  • Jumping to Cursor before Inspector works
  • Five tools on day one
  • Blocking the event loop with sync HTTP calls