What is MCP?
MCP (Model Context Protocol) is an open protocol standard introduced by Anthropic in 2024, enabling standardized connections between AI applications and external tools/data sources through JSON-RPC 2.0 specification, solving the fragmentation problem of AI Agent integration with heterogeneous systems.
Quick Facts
| Full Name | Model Context Protocol |
|---|---|
| Created | November 2024 by Anthropic |
| Specification | Official Specification |
How It Works
MCP serves as the infrastructure layer of the AI Agent technology stack, defining 'what an Agent can connect to'. Its core architecture adopts a client-server model: MCP Client (e.g., Claude Desktop, Cursor) initiates requests, while MCP Server exposes specific capabilities (Resources, Tools, Prompts). The protocol supports two transport methods: stdio (local process communication) and HTTP with SSE (remote services). MCP uses Eager Loading mode, fetching complete JSON Schema definitions of all available tools via tools/list request at session start. This design simplifies implementation but incurs high token consumption (~16000+ tokens). To address this, Anthropic introduced the Skill standard in 2025 as a complement, implementing on-demand loading through Progressive Disclosure. MCP + Skill together form the capability extension system for modern AI Agents: MCP handles 'connection', Skill handles 'guidance'.
Key Characteristics
- JSON-RPC 2.0 Protocol: Standardized request-response communication format with batch calls and error handling
- Three Capability Primitives: Resources (read-only data), Tools (executable operations), Prompts (prompt templates)
- Capability Negotiation: Client and server exchange supported feature lists upon connection
- Dual Transport Support: stdio (local high-performance) and HTTP+SSE (remote scalable)
- Eager Loading Mode: Loads all tool definitions at session start, simple but token-intensive
- Open Ecosystem: Adopted by Claude, Cursor, Zed, Replit, Codeium and other mainstream tools
Common Use Cases
- IDE Integration: Connect to code repositories, execute terminal commands, read/write file systems
- Database Access: Execute SQL queries, retrieve table schemas, data analysis
- API Gateway: Unified wrapper for REST/GraphQL APIs for Agent invocation
- Knowledge Base Retrieval: Connect to vector databases, document systems for RAG applications
- DevOps Toolchain: Integrate CI/CD, monitoring alerts, log analysis systems
Example
Loading code...Frequently Asked Questions
What problem does MCP solve for AI applications?
MCP solves the fragmentation problem of AI Agent integration with external tools and data sources. Before MCP, each AI application needed custom integrations for every tool. MCP provides a standardized protocol so any MCP-compatible client can connect to any MCP server, similar to how USB standardized device connections.
What is the difference between MCP Resources, Tools, and Prompts?
Resources are read-only data sources (like files or database content) that provide context. Tools are executable operations that perform actions (like running queries or sending messages). Prompts are reusable prompt templates that help users interact with specific capabilities. Together they form MCP's three capability primitives.
How do I create my own MCP server?
Use the official MCP SDK (@modelcontextprotocol/sdk for TypeScript/JavaScript or mcp package for Python). Define your server's capabilities (tools, resources, prompts), implement request handlers for each capability, and choose a transport method (stdio for local or HTTP+SSE for remote). The SDK handles protocol details automatically.
What is the relationship between MCP and the Skill standard?
MCP and Skill are complementary standards. MCP handles 'connection' - how agents communicate with external tools. Skill handles 'guidance' - helping agents understand when and how to use capabilities through progressive disclosure. Skill addresses MCP's token consumption issue by loading tool definitions on-demand rather than all at once.
Which AI applications support MCP?
Major MCP-compatible applications include Claude Desktop (Anthropic), Cursor (AI code editor), Zed (code editor), Replit, Codeium, and Sourcegraph. The ecosystem is growing rapidly with new integrations being announced regularly. Check the official MCP documentation for the latest supported clients.