MCPs: Build Your Own
Many teams are now building custom MCPs for their internal tools. Now it's your turn — understand them deeply, learn the anatomy, and build one from scratch. MCPs are the missing layer between AI and your internal tools.
- What Model Context Protocol is and why it changes AI integrations
- The 3 building blocks of any MCP server (Tools, Resources, Prompts)
- When MCP is the right call vs a simpler approach
- Design the tools your own MCP should expose
- Scaffold a working MCP server in TypeScript
- Explain MCP architecture to an engineering colleague clearly enough to get their buy-in
Step 1 of 5 · Read the lesson
What MCP is
Model Context Protocol — an open standard for connecting AI assistants to your tools, data, and prompts. Think USB-C for AI.
Anatomy of an MCP server
- Tools — actions the model can invoke (query DB, call API)
- Resources — data the model can read (files, records)
- Prompts — reusable templated workflows
- Transport — stdio (local) or HTTP/SSE (remote)
MCP vs n8n
n8n is scheduled workflow. MCP is persistent capability. With MCP, your AI can always reach your systems on demand — no trigger needed.
~50 lines to a working MCP
The TypeScript SDK lets you spin a server with one tool in under 50 lines. Start there. Add tools as you discover what you keep copy-pasting into Claude.
The MCP ecosystem — what already exists
Before building a custom MCP, check what's already available. A growing ecosystem of pre-built MCP servers covers: Notion, Linear, GitHub, Slack, Google Drive, HubSpot, Contentful, and many more. If your tool is already covered, you can connect it to Claude in minutes — no building required.
The value of building your own MCP is for internal tools and proprietary data that don't have a pre-built integration: your experiment tracker, your internal analytics API, your custom data warehouse. That's where the leverage is.
Check the official MCP registry at modelcontextprotocol.io/registry before building from scratch.