What is the Model Context Protocol (MCP) and how does it enable AI agent tool use?

The Model Context Protocol (MCP) is an open standard (by Anthropic) for connecting AI agents with external tools and data sources. Architecture: MCP servers expose capabilities (tools, resources, prompts) via a standardized JSON-RPC protocol, and MCP clients (AI applications) discover and invoke these capabilities. Key components: tools (executable functions the AI can call, defined with JSON Schema parameters), resources (data sources the AI can read, like files or database records), and prompts (reusable prompt templates). Benefits: standardized interface means one tool implementation works across all MCP-compatible AI clients, tool providers write a server once and it works everywhere, and AI applications gain instant access to a growing ecosystem of tools. Implementation: create an MCP server using the SDK (TypeScript or Python), define tools with name, description, and input schema, implement tool execution handlers, and expose via stdio (for local) or HTTP/SSE (for remote). Example tools: file system operations (read, write, search files), database queries (execute SQL, browse tables), web interactions (fetch URLs, search the web), and API integrations (GitHub, Slack, email). Discovery: MCP clients discover available tools at connection time, receiving the full tool catalog with descriptions and schemas. The LLM uses these descriptions to decide when and how to invoke tools. Security: MCP servers should validate all inputs, implement authentication for remote servers, restrict tool capabilities to minimum necessary permissions, and log all tool invocations. Ecosystem: growing library of pre-built MCP servers for common services (databases, file systems, APIs), enabling rapid AI agent development without custom integration work.