Model Context Protocol (MCP): The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024, that lets AI assistants connect to external tools, data sources, and services through a common interface instead of custom one-off integrations.
Before MCP, every AI product needed its own connector for every service: one integration for Google Drive, another for Slack, another for your database, rebuilt for each assistant. MCP defines a shared language. A developer builds one MCP server for a service, exposing its tools and data, and any MCP-compatible assistant can use it. Think of it as a universal plug for AI tools.
An MCP server can expose three kinds of things: tools the model can call (search tickets, create a record), resources it can read (files, database rows), and reusable prompts. The assistant, acting as the MCP client, discovers what is available and calls it when useful. Since its release, MCP has been adopted across the major assistants and developer tools, and thousands of community and vendor servers exist for common business software.
For a non-developer, MCP mostly shows up as a settings menu of connectors you can switch on: your calendar, your file storage, your CRM, your project tracker. The questions to ask before enabling one are the same as for any integration. What can it read? What can it change? Who published it? A connector from an unknown source can read whatever it is granted, so treat it like installing software.
Example at work
An account manager enables an MCP connector for her company's CRM inside her AI assistant. Now she can ask "summarize my open deals over $50k with no activity in two weeks" and get a real answer from live data. Her admin configured the connector read-only, so the assistant cannot edit a deal record.
Why it matters
MCP is why AI assistants are quickly becoming able to reach your actual work systems. Knowing what it is helps you understand what a connector can do, ask the right permission questions, and spot when a vendor's "integration" is a standard MCP server you could use elsewhere.
Related terms
- Tool useTool use (also called function calling) is the ability of an AI model to request that an outside tool be run, such as a web search, a calculator, a database query, or a calendar lookup, and then use the result in its answer.
- AI agentAn AI agent is a system that uses an AI model to pursue a goal on its own by planning steps, using tools such as web browsers, files, and software APIs, and adjusting based on results, rather than answering a single prompt.
- API (application programming interface)An API (application programming interface) is a way for one piece of software to talk to another. An AI API lets your own tools, spreadsheets, or automations send a prompt to a model and receive the response, without a person typing in a chat window.
- Agentic AIAgentic AI describes AI systems designed to act with autonomy: they take a goal, break it into steps, use tools, and carry the work through with limited human direction. It is the broader approach; an AI agent is a specific system built that way.
- Workflow automationWorkflow automation connects apps and steps so that a trigger, such as a new form response or an incoming email, automatically runs a sequence of actions, increasingly with an AI step that reads, decides, or writes along the way.