Model Context Protocol (MCP)

What is Model Context Protocol (MCP)?

Model Context Protocol is an open standard that defines how AI applications connect to external data sources and tools through a unified client-server interface. Developed by Anthropic and released as an open specification, MCP eliminates the need for custom integrations between AI assistants and the services they interact with. It standardizes the way context, tools, and resources are exposed to language models, similar to how USB standardized peripheral connections for computers.

How does Model Context Protocol (MCP) work?

MCP uses a client-server architecture where AI applications (clients) connect to MCP servers that expose capabilities. Each server declares what it offers — tools (executable functions), resources (data sources), and prompts (reusable templates) — through a discovery mechanism.

When an AI assistant needs to query a database, read a file, or call an API, it sends a structured request to the appropriate MCP server. The server executes the operation and returns results in a standardized format the model can consume. This decouples the AI application from specific tool implementations.

For example, a developer building an AI coding assistant does not need to write custom GitHub, Jira, and Slack integrations. They connect to existing MCP servers for each service, and the assistant automatically discovers and uses the available tools.

Why does Model Context Protocol (MCP) matter?

Before MCP, every AI application needed bespoke integrations for each external service, creating an N-by-M compatibility problem. MCP reduces this to N+M: build one server per service, one client per AI app, and they all interoperate.

The protocol has seen rapid adoption since its release, with thousands of community-built servers covering databases, cloud platforms, developer tools, and SaaS applications. This ecosystem effect means new AI applications instantly gain access to hundreds of integrations without writing custom code.

Best practices for Model Context Protocol (MCP)

• Scope each MCP server to a single domain or service to keep tool descriptions clear and reduce confusion for the model
• Implement proper authentication and authorization at the server level rather than relying on the AI client to enforce access controls
• Return structured data with clear field names so the model can reason accurately about results
• Version your MCP server's tool schemas to avoid breaking existing client integrations during updates