MCP is an open standard for AI (Artificial Intelligence) models to connect to many different apps and data sources in a consistent way.
This is the language and rules that the clients and servers use to communicate. It defines things like message formats, how a server advertises its available commands, how an AI asks a question or issues a command, and how results are returned. The protocol is transport-agnostic: it can work over HTTP/WebSockets for remote or standalone servers, or even standard IO streams (stdin/stdout) for local integrations. The content of the messages might be JSON or another structured schema (the spec uses JSON Schema for definitions). Essentially, the protocol ensures that whether an AI is talking to a design tool or a database, the handshake and query format are consistent. This consistency is why an AI can switch from one MCP server to another without custom coding – the “grammar” of interaction remains the same.
For example, with MCP an AI model could fetch information from a database, edit a design in Figma, or control a music app – all by sending natural-language instructions through a standardized interface.
MCP follows a client-server architecture.
- MCP Servers: lightweight adapters that expose an applications’s functionality in a standardized way. It knows how to take a natural-language request and perform the equivalent action in the app
- Tool Discovery: which actions/capabilities the application offers
- Command Parsing: They interpret from the AI incoming instructions for exact commands or API calls
- Response Formatting: Format the Output of the Application so that the AI understands it
- Error Handling: Catch exceptions and respond appropriately to the AI
- MCP Clients:
- maintains 1:1 connection to server
- AI connects through the MCP Client to the MCP Server
- Services:
- the actual applications the AI interfaces with