Skip to main content
Glama

Citehound

Get the scoring methodology

get_methodology
Read-onlyIdempotent

Explains how the Citehound AI readiness score is built, read from the scanner’s own check registry so it matches what runs. With no argument it returns the three pillars with their weights (they add up to 100), every check with its category, points, what it tests and why it matters, and the limits of the scan: it measures readiness and not whether any assistant names a brand; it gives half credit for a crawler with any applicable Disallow rule, usually an ordinary path; and it reads the homepage only. With a check label it returns that one check in detail. An unknown label returns an error listing the valid labels. Read-only, fetches nothing external. Use when the user asks how a score is calculated, why a check carries the points it does, or what the scan cannot tell them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
checkNoOptional. A check label exactly as scan_site reports it, e.g. "Page title" or "AI crawler access". Case does not matter. Omit for the whole methodology.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

Score is being calculated.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.