Model Context Protocol

What is MCP

MCP (Model Context Protocol) is an open communication standard in the AI field introduced by Anthropic on November 25, 2024, which can be thought of as "the USB-C port of the AI world". It provides a unified, standardized way for large language models (LLMs) to connect to external data sources and tools. Before MCP, every AI application had to write its own adapter code to connect to databases, file systems, or APIs; with MCP, you only need to write one MCP Server and any MCP-compatible client (Claude, Cursor, Trae, etc.) can use it directly.

In one sentence: MCP is to AI tools what USB-C is to electronic devices—one standard port connects to everything.

Why MCP Matters

The core problem MCP solves is the "N×M problem of tool integration": N AI applications × M tools requires writing N×M sets of adapter code. MCP turns it into N+M—applications implement the MCP client once, tools implement one MCP Server, and they interoperate. In 2025 OpenAI announced MCP integration in the ChatGPT desktop app, marking it as a de facto industry standard. Today, mainstream tools such as Claude Code, Cursor, Trae, Windsurf, and Continue all support MCP.

# Without MCP: every app × every tool needs its own adapter
Claude  ──adapter──> database
Cursor  ──adapter──> database   # reinventing the wheel
Trae    ──adapter──> database

# With MCP: apps and tools each implement once
Claude ─┐
Cursor ─┤── MCP protocol ──< database MCP Server
Trae   ─┘                     filesystem MCP Server
                               GitHub MCP Server

Architecture

MCP uses a client-server architecture: the MCP Client is embedded in an AI application (e.g. Claude Code) and initiates requests; the MCP Server is a separate process that encapsulates specific tool capabilities (e.g. reading files, querying a database, calling an API). The two communicate via JSON-RPC, and the Server exposes three categories of capabilities to the Client: Tools (executable operations), Resources (readable data), and Prompts (preset prompt templates). A single Client can connect to multiple Servers at once.

# Three core MCP capabilities
Tools:     # Executable operations (with side effects)
  - query_database(sql)
  - send_email(to, subject)
  - create_issue(title)

Resources: # Readable data (read-only)
  - file:///project/README.md
  - database://users/schema

Prompts:   # Preset prompt templates
  - "Review the security of this code: {code}"

# Configure an MCP Server in Claude Code
# ~/.claude/claude_desktop_config.json
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path"]
    }
  }
}

How to Use MCP

For end users, using MCP is zero-code: just add an off-the-shelf MCP Server to the config file of an MCP-enabled tool (e.g. Claude Code, Cursor). For developers, the official SDKs (TypeScript/Python) let you quickly write custom MCP Servers to expose internal systems to AI. The community already has hundreds of open-source MCP Servers covering scenarios such as file systems, databases, GitHub, Slack, and browsers.

# 1. Use an off-the-shelf MCP Server (filesystem example)
# Add to the Claude Code config file:
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem",
               "/Users/me/projects"]
    }
  }
}

# 2. Configure in Cursor: Settings → MCP → Add Server

# 3. Develop your own MCP Server (Python SDK)
pip install mcp
# from mcp.server import Server
# Define your tools and expose them to all MCP clients

# 4. Common community MCP Servers
# @modelcontextprotocol/server-github    # GitHub operations
# @modelcontextprotocol/server-postgres  # PostgreSQL queries
# @modelcontextprotocol/server-puppeteer # Browser automation

Tip: MCP Servers run locally and data doesn't pass through third parties, making them suitable for enterprise intranets and privacy-sensitive scenarios.