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Glossary

What is MCP? The Model Context Protocol

A plain explanation of the Model Context Protocol: what it is, how its client, server and tools fit together, why businesses care, and how to keep it safe.

Updated

Definition

Model Context Protocol

What is MCP? The Model Context Protocol (MCP) is an open standard that lets AI models connect to tools and data sources through one common interface, instead of a custom connection for every pair of systems.

In short: One plug that lets AI reach your tools.

What is MCP and where it came from

Without a standard

  • One custom link per AI and tool pair
  • Each link built and fixed separately
  • Switching AI models means rebuilding

With MCP

  • One common interface for all tools
  • Tools offer a connection once
  • Models can be swapped more easily

Anthropic introduced the protocol in November 2024, according to Itential. Its goal is to standardize how AI systems reach outside tools, data sources and automation platforms.

The official MCP documentation calls it an open protocol with support across many clients and servers. Google Cloud describes it as an open standard built to solve the same connection problem.

Think of it as a shared plug shape. Any AI app that speaks MCP can use any tool that offers an MCP connection.

How MCP works with client, server and tools

  1. 01

    Client asks

    The AI app asks what the server offers.

  2. 02

    Server lists tools

    It describes the actions and data it exposes.

  3. 03

    Model picks a tool

    The model chooses the one that fits the task.

  4. 04

    Result returns

    The answer goes back into the conversation.

Three parts do the work. The client sits inside the AI application, such as an assistant or an agent.

The server sits in front of a tool or data source and describes what it can do.

The client asks the server what is available. The model then picks a tool, the client sends the request, and the server returns the result for the model to use in its answer.

A model context protocol example: an assistant asks a CRM server for a customer's open deals, reads the reply and drafts a follow-up email.

Developers often test a server with a model context protocol inspector, a debugging tool that shows what a server offers.

Why MCP matters for businesses

  • CRM

    Look up deals and contacts.

  • Files

    Read documents and policies.

  • Databases

    Query records on demand.

  • Inbox

    Draft and send messages.

Most useful AI work needs access to what your company already uses: the CRM, shared files, databases, email and accounting tools. MCP gives AI a standard way to reach them.

This cuts the one-off integration work and makes it easier to add a tool later.

It also pairs well with agents, which need tools to act. See AI agents and our AI integration service for how connections get built.

MCP is not the only way to give AI your information. For a comparison with document retrieval, read RAG vs MCP.

Security and permissions

Good practice

  • Read-only access by default
  • Approval before changes
  • Log every tool call
  • Know who runs each server

Risky habits

  • Full access for every tool
  • Unknown third-party servers
  • No record of actions

A connection is also a doorway. A server decides what a model may read or change, so access should be narrow, with read-only rights wherever possible.

Keep a person in the loop for steps that change data or spend money.

Log every tool call so you can see what the model did. Our human in the loop page explains the approval pattern.

Treat each server like any other integration: know who runs it, what data it touches and who can switch it off.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI integration. They named 96 companies, and 82% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named373746
Sites cited mostibm.com and accenture.com (5 of 10)rtslabs.com and iternal.ai (2 of 10)esparkinfo.com (4 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 7 companies appeared in all three. Google showed an AI Overview for 9 of the 10 questions.

FAQ

MCP questions

  • A shared language between AI apps and tools. Once a tool offers an MCP connection, any compatible AI can use it without a custom build.

  • Connecting AI to real systems: a CRM, files, databases and email. It lets an assistant fetch current data and take actions instead of only answering from memory.

  • In IT, MCP means this AI protocol, not other uses of the same letters. It standardizes how AI applications call tools and read data sources.

  • No. An API is one tool's own interface. MCP sits on top and gives AI models a common way to discover and use many tools, often wrapping existing APIs.

  • Yes, with limits. Give each server narrow, read-only access where possible, require approval for risky steps and keep logs of tool calls.

  • RAG retrieves documents to ground an answer. MCP connects a model to tools and live systems it can act on. Many systems use both. See RAG.

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