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Check AI readiness

geo_check
Read-only

Check whether AI assistants (ChatGPT, Claude, Perplexity, Gemini) can read and cite a page. Returns the AI readiness score, which AI crawlers robots.txt blocks, whether llms.txt exists, and only the AI-related problems with how to fix them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull public URL of the page, with http:// or https://.
langNoLanguage of names and recommendations. Default: en.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description goes beyond them by enumerating what the result contains — readiness score, robots.txt-blocked crawlers, llms.txt presence, and AI-specific problems with fixes — which is valuable since there is no output schema. It says nothing about latency, auth, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler: the first establishes the operation and audience, the second enumerates the return payload. The most important clause (what is checked) is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly carries the return-value burden and does so thoroughly, listing the score, blocked crawlers, llms.txt status, and fixable problems. It is slightly incomplete in not clarifying how it differs from audit_url/verify or when lang matters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both the url and lang parameters are already documented in the schema. The description adds no format or constraint detail beyond what the schema provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Check whether AI assistants ... can read and cite a page') and names the concrete platforms checked, so the agent knows exactly what operation this performs. It does not, however, distinguish itself from the sibling tools audit_url and verify, leaving overlap ambiguity unresolved.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use statement, no prerequisites, and no routing advice relative to audit_url or verify. Usage is only implied by the word 'page' and the AI-readiness framing; an agent has to guess which of the three check tools to pick.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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