Model Context Protocol (MCP)

Before MCP, every AI tool built its own custom integrations for databases, APIs, and external services. This meant duplicated work across tools and vendor lock-in for users. MCP solves this by defining a standard protocol: tool makers build one MCP server, and it works with every AI agent that supports MCP—Claude Code, Cursor, Windsurf, and others.

MCP uses a client-server architecture. The AI agent (client) connects to MCP servers that expose tools, resources, and prompts. When the agent needs to query a database, search documentation, or call an API, it sends a standardized request to the appropriate MCP server. The server handles the actual interaction and returns structured results the agent can reason about.

// Example: MCP server configuration in Claude Code
// ~/.claude/settings.json
{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": { "GITHUB_TOKEN": "ghp_..." }
    },
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": { "DATABASE_URL": "postgresql://..." }
    }
  }
}

MCP servers are available for GitHub, PostgreSQL, Slack, Google Drive, file systems, and dozens of other services. Check the MCP server registry for a full list of community-built integrations.