MCP (Model Context Protocol)
What is MCP (Model Context Protocol)?
Definition
MCP (Model Context Protocol) is an open standard that defines how AI models connect to external tools, data and services through one consistent interface. Instead of building a custom integration per app, a model speaks MCP to any compliant "server" — think of it as a universal plug between AI and the rest of your systems.
Table of contents
MCP (Model Context Protocol) is an open standard — introduced by Anthropic and now widely adopted — for connecting AI models to the tools and data they need. It has been called "USB-C for AI": one port, many devices.
The problem MCP solves
Every AI agent needs to reach real systems — databases, file stores, CRMs, search. Without a standard, each connection is a bespoke integration, and the number of integrations explodes as you add models and tools. MCP replaces that M×N mess with a single, shared protocol.
How MCP works
- MCP server: wraps a tool or data source (e.g. a database, a ticketing system) and exposes its capabilities in a standard way.
- MCP client: lives inside the AI application and talks to any server using the same protocol.
- Capabilities: servers offer tools (actions the model can call), resources (data it can read) and prompts (reusable templates).
Because the interface is consistent, you can add a new data source to your AI without rewriting the model integration.
Why it matters
MCP makes AI systems modular and portable. Swap the underlying LLM, reuse the same tools; add a new tool, every model gets it. For companies that means faster integration, less lock-in and a cleaner security boundary — you control exactly what each server can do.
In practice
In the AI operations platform behind this site, a self-built MCP server is the single entry point — with scoped permissions — over an organization-wide memory store, so dozens of agents reach the same data and tools through one consistent, governed interface.
Summary
MCP is the standard plug between AI models and the real world. It turns one-off integrations into reusable building blocks — the foundation for scalable, maintainable AI agents.
Frequently asked questions
Who created MCP?
MCP was introduced by Anthropic as an open standard and has since been adopted across the industry, with a growing ecosystem of MCP servers for common tools and data sources.
Do I need MCP to build an AI agent?
No, but it helps. You can wire tools directly, yet MCP standardizes those connections so they are reusable, swappable and easier to secure — especially once you run many tools or many agents.
More from the Wiki-Lexikon
What is an AI agent?
An AI agent is software that uses a language model to plan and act toward a goal — calling tools, making decisions and running multi-step tasks autonomously. Definition, how it works and examples.
What is an LLM (large language model)?
A large language model (LLM) is an AI trained on huge amounts of text to predict and generate language. Definition, how it works, tokens, context window and where the limits are.
What is RAG (retrieval-augmented generation)?
RAG (retrieval-augmented generation) feeds an LLM relevant, current data at query time so its answers are grounded in your facts — not just its training. Definition, how it works and why it matters.
What is AI automation?
AI automation uses language models and AI agents to handle tasks that need understanding and judgment — not just fixed rules. Definition, how it differs from classic automation, and real examples.