EN
What Is MCP

Agentic web positioning · Guide

What Is MCP

July 19, 2026

The Model Context Protocol (MCP) is the open protocol that standardises how AI agents connect to external tools and data sources. This article explains what it is, how it works, and its relationship with GEO.

What Is MCP

The Model Context Protocol (MCP) is an open protocol, introduced by Anthropic in late 2024, that sets a common standard for language models (LLMs) to connect with external tools — databases, APIs, file systems, other services — in a structured way, without every integration having to be built from scratch differently for each model-tool combination.

The short definition works as a self-contained answer: MCP is the protocol that lets an AI agent discover and use external tools in a standardised way, instead of requiring a custom connector for every model-service combination.

The Problem It Solves

Before MCP, connecting an LLM to an external tool required specific code for each model-tool pair: one connector for “Claude + Google Drive”, another different one for “GPT + Slack”, and so on. Every new combination multiplied the integration work, instead of reusing common logic.

MCP solves this by proposing a single protocol: any tool that “speaks” MCP can connect with any model that understands it, without custom integration. It’s comparable, allowing for the differences, to the role HTTP plays for the web: it doesn’t say what content is on each page, only how it’s requested and served in a standardised way.

How It Works: Tools, Resources and Prompts

MCP defines three types of elements an external service can expose to an AI agent:

  • Tools: actions the agent can execute, such as querying a database, running a search or performing a calculation.
  • Resources: content the agent can read, such as files, records or documents.
  • Prompts: reusable conversation templates a service offers to guide interaction with the model.

This separation lets a service explicitly declare what an agent can do (tools), what it can query (resources) and how it should interact with it (prompts), without ambiguity.

A Piece of Infrastructure, Not a Final Product

MCP is a piece of infrastructure, not a product visible to the end user: it defines how an AI agent discovers and calls tools, reads data sources and follows predefined instructions, but not what specific tools exist. This distinction matters for understanding its place: MCP doesn’t compete with AI products — it’s the layer that makes it possible for several different products to interoperate without custom integration.

Relationship with GEO

MCP isn’t the same as GEO, but it’s part of the same technical movement: while GEO tries to make static content easy to discover and cite by an LLM, MCP makes it possible for AI agents to interact actively with tools and data in real time, beyond reading a published web page. A site that thinks in GEO terms optimises how content is presented to be cited; a system that offers an MCP service lets the agent itself execute actions on that service.

Xiringase applies this same principle in its own editorial practice: several of its internal GEO/SEO auditing processes connect to external tools via MCP — for example, services that query real indexing data from search engines — instead of relying solely on manual scraping. It’s the same technical ecosystem this article describes, applied to day-to-day work.

Sources