The Problem AI Tooling Has Always Had
Every AI coding tool — Claude, Copilot, Gemini — works in isolation. They can read your codebase, write code, and run tests. But connecting them to your internal APIs, databases, or workflows? That's where the fragmentation starts.
Each tool has its own way of connecting to the outside world. Claude uses MCP (Model Context Protocol). Copilot has its Extensions API. Gemini has its own integrations. If you build tooling for one model, you can't easily port it to another. That's the lock-in tax developers pay.
What MCP Actually Is
MCP is an open protocol from Anthropic that defines how AI models communicate with external data sources and tools. Think of it as a universal adapter — if a tool supports MCP, any MCP-compatible model can use it without custom integration work.
The core building blocks:
- Hosts: the AI application (Claude Desktop, Cursor, etc.)
- Clients: built into the host, connect to servers
- Servers: lightweight programs that expose tools and data sources via MCP
Why This Matters Right Now
The agentic AI wave — where models act autonomously across multiple steps — has exposed the limits of one-off integrations. When an agent needs to query your database, call your API, read your docs, and execute code, those integrations can't be hardcoded per tool.
MCP standardizes that layer. An agent can discover and use MCP servers at runtime, building its toolchain dynamically. The protocol supports:
- Resources: read-only data (file contents, API responses, documentation)
- Tools: executable functions the model can call
- Prompts: reusable prompt templates
Early Ecosystem
Anthropic open-sourced the MCP spec and SDK. The community is building servers for:
- PostgreSQL, SQLite, MongoDB
- GitHub, GitLab repositories
- Slack, Notion, Google Drive
- File systems and web content
- Custom REST APIs
What Builders Should Do
If you're building AI-powered developer tools or internal agent infrastructure, supporting MCP is low-effort with high leverage. Your tool works across models, and your users get a consistent experience regardless of which model they choose.
The protocol is early, but the direction is clear: the winners in AI tooling won't be the most capable models — they'll be the most well-connected ones.
*Anthropic released the MCP specification as open source. The protocol is designed to be extensible and model-agnostic.*