A concrete example
Instead of hacking together a different integration for each tool, you add an MCP server that exposes the right actions to the agent.
Why it matters
Agents become useful when they can read, search, and act inside your real tools.
You run into it in Claude Desktop, Claude Code, Cursor, and the connectors that give access to GitHub, Notion, files, or databases.
Don't mix it up with
Tool use / Function calling: Tool use lets a model call an external tool instead of only replying with text.
Agentic workflow: An agentic workflow is a sequence of steps where the AI plans, uses tools, checks its work, and keeps going until it hits the result.
Common mistakes
- Installing too many connectors without knowing what they allow.
- Giving access to sensitive data with no safeguards.
- Confusing MCP with an AI model.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: Instead of hacking together a different integration for each tool, you add an MCP server that exposes the right actions to the agent.
- I keep the main trap in mind: Installing too many connectors without knowing what they allow.
Quick questions
What is MCP in AI?
MCP, short for Model Context Protocol, is a standard that lets an AI connect cleanly to tools and data.
Where will I run into MCP?
You run into it in Claude Desktop, Claude Code, Cursor, and the connectors that give access to GitHub, Notion, files, or databases.
Which word should I read next?
Start with Tool use / Function calling, Agentic workflow, Guardrail.