Features
- Standardized protocol for AI tool integration
- Server/client architecture for extensibility
- Resources, tools, and prompts as first-class concepts
- Transport-agnostic (stdio, HTTP, SSE)
Pros
- Universal standard for AI tool connectivity
- Growing ecosystem of pre-built MCP servers
- Works across multiple AI platforms and editors
Cons
- Still an evolving specification
- Server development requires understanding the protocol
- Debugging MCP connections can be tricky
Overview
The Model Context Protocol (MCP) is an open protocol created by Anthropic for connecting AI models to external data sources and tools. It defines a standardized way for AI applications to discover and use capabilities provided by MCP servers, enabling a plug-and-play ecosystem of AI integrations.
MCP uses a client-server architecture. MCP servers expose resources (data to read), tools (actions to perform), and prompts (templates to use). MCP clients (like Claude Code, Claude Desktop, or custom applications) discover and use these capabilities through the protocol.
The protocol is transport-agnostic, supporting stdio (for local processes), HTTP with Server-Sent Events (for remote servers), and other transports. This flexibility allows MCP servers to run locally alongside an AI application or as remote services.
When to Use
Use MCP when building tools and integrations that should work across multiple AI platforms. Build MCP servers to expose your services to AI assistants, or use existing MCP servers to extend your AI application’s capabilities.
Getting Started
npm install @modelcontextprotocol/sdkimport { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
const server = new McpServer({ name: 'my-server', version: '1.0.0' })
server.tool('greet', { name: { type: 'string' } }, async ({ name }) => ({
content: [{ type: 'text', text: `Hello, ${name}!` }]
}))
const transport = new StdioServerTransport()
await server.connect(transport)