What Is MCP? Model Context Protocol in Plain Terms

MCP (Model Context Protocol) is an open standard for connecting AI assistants and agents to outside tools and data: a database, a CRM, Slack, a file system. Anthropic introduced it in late 2024, and it's now supported across most major AI platforms.

How does MCP work?

There are three roles:

• Host: the AI application you use, such as a chat app, an IDE or an agent.
• Client: the connection manager inside the host. It keeps one connection per server.
• Server: the program that wraps a tool or data source and describes what it can do.

Servers expose three kinds of things: tools the model can call (run a query, create a ticket), resources it can read (files, records), and prompts (reusable templates). Messages travel as JSON-RPC. When the host connects, it asks each server what it offers, and those descriptions are handed to the model so it knows what it can call.

What problem does MCP solve?

Before a shared standard, every AI app needed custom code for every tool. With N apps and M tools, that's N × M integrations. MCP turns it into N + M: each app speaks MCP once, and each tool ships one server. That's why hundreds of servers exist for warehouses, CRMs, issue trackers and more.

Why can more MCP tools make an agent worse?

Every connected tool has a description, and in the default setup those descriptions are loaded into the model's context before your question. Two things happen as the list grows:

• Context gets crowded. Tool schemas take up tokens that would otherwise go to your question, your data and the model's reasoning. You pay for them on every turn.
• Choosing gets harder. With dozens of similar tools, the model picks the wrong one more often, or calls the right one with the wrong parameters.

In analytics this compounds drift. An agent connected to the CRM, the app database and billing has more routes to an answer, so the same question can take a different path each time. The benchmark numbers are in MCP Is Making Your AI Worse, and the consistency side is in Metric Consistency in AI Analytics.

How should I use MCP well?

• Connect only what you use. Audit your servers and remove the ones the agent rarely calls.
• Prefer fewer, sharper tools. One well-described tool beats five overlapping ones. For analytics, exposing a semantic layer's defined metrics as tools beats handing the agent raw SQL access to every source.
• Load tools on demand. Some hosts support tool search or deferred loading, where the model sees a short index and pulls full definitions only when needed. Turn it on if yours does.
• Treat server updates as changes. A server owner can change a tool's description at any time, which changes how the model uses it. Pin versions where you can.

Common pitfalls

• Assuming "it has an MCP server" means the AI will use that tool well.
• Connecting every available server "just in case."
• Installing community servers without checking what they can access. Read-only access is the safe default for data sources.