MCP Explained: What It Is, Why Developers Are Paying Attention, and Where to Find the Best Tools

If you've spent any time building with Claude, you've probably run into MCP. Short for Model Context Protocol, MCP has quickly become one of the most important infrastructure primitives in the agentic AI stack — and if you're not using it yet, you're leaving a lot of capability on the table.

This post covers what MCP is, why it matters, and which tools in the MCP ecosystem are worth your time.

What Is MCP?

MCP — Model Context Protocol — is an open standard developed by Anthropic that defines how AI models communicate with external tools, data sources, and services. In plain terms: MCP is the protocol that lets an AI model like Claude interact with real-world systems in a structured, predictable way.

Before MCP, connecting an LLM to external tools was custom engineering work. Every integration had its own implementation, its own quirks, and its own failure modes. MCP standardizes this: tools expose a consistent interface, models learn to use it, and the whole system becomes composable.

Think of it like USB-C for AI tools. Before USB-C, every device had its own cable. After, you just plug in. MCP is doing the same thing for the relationship between LLMs and the services they work with.

Why MCP Matters for Developers Building Agentic Systems

Agentic AI — systems where an AI model takes sequences of actions, not just single responses — requires reliable tool use. An agent that can search the web, query a database, read files, and call APIs is far more capable than one that can only reason about text.

MCP makes this tractable by standardizing how tools are described and called. When you build an MCP server, you're creating a service that any MCP-compatible model can use. When you use a pre-built MCP tool, you're plugging in battle-tested functionality without writing custom integration code.

In 2026, MCP has matured significantly. The ecosystem has expanded beyond Anthropic's own tools to include database connectors, browser control, file system access, API integrations, search tools, and code execution environments.

How MCP Works (Technically)

An MCP setup has three parts: the host (the AI application, like Claude Desktop or a custom agent), the MCP client (the connection layer), and the MCP server (the tool itself).

MCP servers expose tools (callable functions), resources (data the model can read), and prompts (reusable templates). When the model decides it needs a tool, it calls it through the MCP interface. The server handles the actual execution and returns a structured response. The model reads it, reasons about it, and decides what to do next.

This loop — decide, call, read, reason — is the core of agentic behavior. MCP makes each step clean and well-defined.

Where to Find and Evaluate MCP Tools

BestMCPTools.org is a curated directory of the best MCP tools and servers, organized by category, with information on use cases, integrations, and community standing.

If you've built an MCP server worth sharing, or found one that deserves more visibility, submit it to the directory.