MCP servers: Vital AI agent infrastructure
The Model Context Protocol, developed by AI company Anthropic, aims to standardize how LLMs interact with external data sources and tools bidirectionally and in a memory-persistent way to improve their context for reasoning. This is critical for building AI agents and for vibe coding, a development practice in which LLMs are guided to build entire applications based on natural language prompts from humans.
Released less than a year ago, the protocol has seen rapid adoption with tens of thousands of servers — applications that link LLMs to specific services and proprietary tools — now published online. Anthropic itself has published reference implementations of MCP servers for interacting with Google Drive, Slack, GitHub, Git, Postgres, Puppeteer, Stripe, and other popular services. In March, OpenAI adopted MCP, and Google announced plans in April to integrate MCP with its Gemini models and infrastructure.
There are also MCPs that integrate with popular AI-assisted integrated development environments (IDEs) such as Cursor, Windsurf, and Zed. In addition to accessing external tools, MCPs can interact with local file systems, build knowledge graphs in system memory, fetch web content using local command line tools, and execute system commands, among other tasks.
