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Structural Integrity 30%
Title, headings, canonical, Open Graph, semantic HTML and language attributes.
AI Extractability 35%
JSON-LD schema, paragraph length, lists, date signals, internal links and breadcrumbs.
Content Clarity 15%
Sentence length, heading density and Flesch readability — with Greek language support.
Authority & Trust 20%
Author attribution, social profiles, image alt text, meta robots and robots.txt AI access.
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What is MCP (Model Context Protocol)?

MCP is an open protocol that lets AI applications connect to external tools and data sources through one standard interface.

Published

Definition

MCP (Model Context Protocol) is an open protocol, introduced by Anthropic in late 2024, that lets AI applications connect to external tools and data sources through a single standard interface instead of a custom integration for each one.

An MCP server exposes tools. An AI client — Claude, ChatGPT, an agent framework — discovers those tools and calls them during a conversation.

Why It Matters for AI Visibility

Everything else in AI visibility is about being read. MCP is about being used.

A site with an MCP server stops being a document the model summarizes and becomes a capability the model can invoke. That is a different relationship: instead of hoping to be cited when someone asks about your subject, your service runs inside the conversation where the question was asked.

For most content sites this is not yet relevant. For anything with a function behind it — a calculator, a lookup, an analyzer, a booking system — it is the difference between being described and being used.

Example

hey-eye runs an MCP server at https://hey-eye.gr/mcp exposing two tools: analyze_url and test_ai_crawler_access. Once connected, you can ask your assistant to analyze a page and the score arrives in the chat.