The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI assistants connect to external tools, data sources, and structured prompts. Think of it as the USB-C of AI integrations: a single, well-defined interface that works across many different AI clients and tool providers.
Why MCP Exists
Before MCP, every AI integration was a one-off. You’d write a custom integration for Claude, another for OpenAI, and yet another for Cursor — each with slightly different conventions, auth models, and error handling. This fragmentation made it expensive to expose your services to AI assistants and created maintenance overhead that scaled poorly.
MCP solves this by defining a shared protocol that any compliant AI client can speak. Build your server once, and it works everywhere MCP is supported.
The Three Primitives
MCP organizes the capabilities a server can expose into three core primitives:
Tools
Tools are executable functions the AI can invoke. They accept structured input (defined as a JSON schema), perform an action, and return structured output. A tool might search a database, send an email, call an external API, or create a file. Tools are the most interactive primitive — they let AI assistants take action on your behalf.
export const searchProducts = tool({
name: "search_products",
description: "Search the product catalog by keyword",
parameters: z.object({
query: z.string().describe("Search term"),
limit: z.number().optional().default(10),
}),
execute: async ({ query, limit }) => {
return db.products.search({ query, limit });
},
});
Resources
Resources are data the AI can read — files, database records, API responses, configuration values. Unlike tools, resources are typically read-only and identified by a URI. An AI client can subscribe to resources that change over time, receiving updates as they happen.
Resources are ideal for giving an AI assistant context about the current state of a system without requiring it to explicitly call a function.
Prompts
Prompts are reusable message templates the AI client can offer to users. A prompt might be “Explain this error” or “Summarize the latest deployment” — a predefined interaction pattern that combines context from resources with a specific instruction.
Transport Mechanisms
MCP supports multiple transport layers:
- stdio: The server runs as a child process and communicates over standard input/output. Simple and widely supported.
- HTTP + SSE: The server runs as a web service. Enables remote servers, multi-client connections, and server-sent events for streaming.
- WebSocket: Bidirectional streaming, useful for low-latency applications.
Most development setups start with stdio. Production deployments typically use HTTP.
The Server Lifecycle
An MCP server goes through a well-defined lifecycle:
- Initialize: Client and server exchange capabilities and version information.
- List: The client discovers available tools, resources, and prompts.
- Invoke: The client calls tools or reads resources as needed.
- Notify: The server can push updates to subscribed resources.
- Shutdown: Clean teardown with graceful error handling.
Why Automatic Generation Matters
Writing an MCP server from scratch requires understanding the full spec: capability negotiation, schema validation, error codes, transport handling, and the subtle behaviors that make clients happy. For a small tool, this might be an afternoon. For a large codebase with dozens of endpoints, it’s a significant investment that has to be maintained as your API evolves.
MCPfy automates this translation layer. It analyzes your codebase — reading function signatures, API routes, type definitions, and docstrings — and generates a production-ready MCP server that accurately reflects what your code actually does. The generated server is plain TypeScript you can read, modify, and commit to your repository. It’s not a runtime wrapper or a black box; it’s real code you own.
The result: your team ships MCP-compatible tooling in minutes rather than days, with no ongoing protocol maintenance burden.
Getting Started
To try MCP yourself, connect a repository on MCPfy.io and generate your first server. The platform walks you through connecting it to Claude, Cursor, or any other MCP-compatible client you use. The whole flow — from repository connection to a working AI integration — takes under five minutes.