mcps explained simply

Published: November 22, 2025 · 2 min read

Right now, LLMs are incredibly capable, but they only work with the data you provide, such as CSV uploads, code snippets, pasted text, or other uploaded files. MCP servers change that. You can connect a database, API, repository, or cloud storage once, and any model that supports MCP can use it. That’s huge because it moves AI from “talking” to “doing.”

Agents are where it gets interesting. An agent is an LLM augmented with a set of tools and a goal. It doesn’t just answer a question but it can plan a sequence of actions, call APIs, query databases, process the results, and decide what to do next. Without access to live tools and systems, an agent is only simulating actions in conversation. MCP servers give agents standardized access to real-world systems so they can execute systems safely and consistently. Agents can query, write, and act across systems without building a new integration for every tool or model update.

The ecosystem is already growing fast. MCP servers exist for PostgreSQL, MySQL, SQLite, GitHub, Slack, Google Calendar, Puppeteer, and more. Every new server expands what agents can do without changing the underlying models. Open source means anyone can build new servers, extend existing ones, and experiment with different workflows.

Overall, MCP servers are the infrastructure that lets agents interact with real systems. Combine an agent with MCP, and you get AI that can actually take action. This makes it possible to automate systems, extract insights, and integrate with your stack in ways that just chatting with a model never could.