What Is MCP?
Model Context Protocol is Anthropic's open standard for connecting AI models to external systems. Instead of each AI app building custom integrations, MCP provides a universal protocol. An MCP server exposes capabilities (tools, data, prompts); an MCP client (Claude, Cursor) uses them. Think of it as USB-C for AI integrations.
MCP Architecture
Three primitives: Tools (functions the AI can call — query database, send email, create file), Resources (data the AI can read — documents, database rows, API responses), Prompts (pre-defined prompt templates with parameters). Clients discover available capabilities via the protocol.
Building MCP Servers
Use the official Python or TypeScript SDK. Define tools with JSON Schema for parameters. Handle tool calls with async functions. Expose resources for data access. A simple MCP server (database query tool) takes ~50 lines of code. Deploy as a local process or remote service.
Practical Use Cases
Connect AI to: your company's internal APIs, database for natural language queries, file system for code generation, CI/CD for deployment status, monitoring for incident investigation. MCP turns AI assistants into actual automation tools that interact with your infrastructure.
MCP Ecosystem
Growing library of community MCP servers: GitHub (issues, PRs), Slack (messages, channels), PostgreSQL (queries), filesystem, web scraping. Install servers via configuration in Claude Desktop or Cursor. The ecosystem is expanding rapidly as more AI apps adopt the protocol.
Conclusion
MCP is becoming the standard for AI-tool integration. If you build internal tools or APIs, creating an MCP server makes them accessible to every MCP-compatible AI app. Start with a simple tool server for your most common workflow and expand from there.