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AI Visibility Audit

audit_ai_visibility
Read-onlyIdempotent

Audits a live public website and scores (0–100, grade A–F) how well AI assistants (ChatGPT, Claude, Perplexity) and search engines can find, read and cite it. Checks robots.txt rules for AI and search crawlers, server-rendered text, business and FAQ structured data, Open Graph, llms.txt, sitemap, HTTPS, mobile viewport and response time. Returns each check with a plain-English fix, the top 3 fixes, and a shareable report link. Takes up to ~20 seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWebsite to audit, e.g. example.com or https://www.example.com/

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint), and the description adds genuinely new operational context: a ~20 second latency expectation, the fact that the target must be live and public, and the shape of the return (per-check fixes, top 3 fixes, shareable report link). It stops short of documenting failure behavior for unreachable or blocked sites, so it is not a 5.

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

Conciseness4/5

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

Front-loaded with purpose and scoring scale, then a compact enumeration of checks, then return shape, then runtime — a logical order with no filler. The long check list is dense but each item is informative about scope, though it could be trimmed slightly.

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 assumes the burden of describing returns (per-check results with fixes, top 3 fixes, report link) and discloses the latency. It leaves error/edge-case behavior (unreachable site, redirects, non-HTML targets) unstated, which is the main remaining gap for a single-param, no-output-schema tool.

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 coverage is 100% and the single url parameter already documents format with an example, so the schema carries the burden. The description adds only an indirect constraint ('live public website'), which slightly narrows acceptable inputs but adds no syntax or format detail beyond the schema. Baseline 3.

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

Purpose5/5

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

Specific verb (audits) + resource (a live public website) + explicit output (0–100 score, A–F grade) and the exact audience (AI assistants and search engines). It also enumerates the check categories, so an agent can tell precisely what this does versus the unrelated calculate_/build_ siblings without opening any schema.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: 'a live public website' signals the input must be publicly reachable (not localhost or auth-gated), which is useful context. However, there is no explicit when-to-use guidance, no mention of prerequisites or when the audit is inappropriate, and no alternative tool named.

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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