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Lekta — AEO/GEO Audit for AI Answer Engines

AEO / GEO audit: grade one page A+ to F

lekta_audit
Read-only

Audits ONE live page for AEO (Answer Engine Optimization), also called GEO (Generative Engine Optimization): whether ChatGPT, Claude, Perplexity, Google AI Overviews and Bing can reach it, read it and lift answers from it. Fetches the page the way those engines do — a plain HTTP GET as LektaBot, then a headless-Chromium render pass — and grades it A+ to F across four weighted layers: Access 25% (robots.txt permission for 38 published AI crawler tokens, counting vendor user agents and robots.txt control tokens alike: GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, Googlebot, bingbot, CCBot and more), Indexability 25% (noindex / nosnippet / canonical controls, and how much main content survives with JavaScript off), Answerability 30% (concrete data density, external evidence, semantic section structure, hedging), Recency 20% (a machine-readable and visible last-updated date). Returns Markdown under fixed headings: "## Verdict" (grade, score, per-layer scores, and the URL actually measured after redirects), "## Top issues" (failing and warned checks ranked by points lost, tagged critical/serious/moderate/minor, with evidence), "## Fixes" (the full remediation text for each). Unmeasured layers cap the grade at B and say so. USE THIS for "run an AEO audit", "run a GEO audit", "why is my page not cited in AI answers", "can ChatGPT / Claude / Perplexity fetch and read this page", "check my robots.txt for AI crawlers" — and again after every deploy to re-measure. The grade measures eligibility preconditions (access, indexability, extractable answers, dates); it does not observe or predict whether any engine cites the page. DO NOT use it when code will parse the result (lekta_report is the same run as JSON), when an audit already exists and you only want the ranked work list (lekta_fix_plan, which never fetches), or to compare two runs (lekta_diff). It scores one page, not a site: no crawling, no sitemap expansion. COST AND BEHAVIOUR: read-only — the audited site is never modified. It READS AND GRADES robots.txt rather than obeying it as a fetch gate: the audit runs even where LektaBot is disallowed, and the report flags that. A blocking call — a live fetch plus a render pass, cut off at a 75 s hard limit. NOT idempotent: a URL not audited in the last 15 minutes starts a fresh run and spends one slot of the daily MCP quota (free plan default 10 fresh audits per UTC day across 1 hostname; every result prints the count used and the host slot in use), while a repeat inside that window replays the cached measurement for free. An unreachable, blocked or HTTP-error target still returns a report, graded U for "unknown" — never F. Only these return an error instead of a grade: a timeout or a server-side engine failure, an exhausted daily quota or host slot, 2 audits already in flight for this account, or the per-target ceiling of 1 audit per minute and 5 per hour.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesRequired. The one absolute http(s) page URL to audit, e.g. "https://example.com/pricing". A bare host+path ("example.com/pricing") is accepted and upgraded to https; the #fragment is dropped and the hostname lower-cased, but the query string is kept and is part of the page identity (?variant=b is a distinct target). ONE page per call — not a domain, a sitemap or a list. Prefer a representative content page over the homepage: scoring is per-URL, not per-site. Redirects are followed and the verdict names the final measured URL, but the audit is STORED under the URL you pass here — give lekta_fix_plan and lekta_diff this same string, not the redirect destination.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / url / description
      Previous value: -"Absolute http(s) URL of ONE page, e.g. https://example.com/pricing. Not a domain list, not a sitemap — audit a representative content page, not only the homepage."New value: +"Required. The one absolute http(s) page URL to audit, e.g. \"https://example.com/pricing\". A bare host+path (\"example.com/pricing\") is accepted and upgraded to https; the #fragment is dropped and the hostname lower-cased, but the query string is kept and is part of the page identity (?variant=b is a distinct target). ONE page per call — not a domain, a sitemap or a list. Prefer a representative content page over the homepage: scoring is per-URL, not per-site. Redirects are followed and the verdict names the final measured URL, but the audit is STORED under the URL you pass here — give lekta_fix_plan and lekta_diff this same string, not the redirect destination."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes far beyond the annotations: discloses the 75 s hard cut-off, the 15-minute cache window, the daily quota and per-target rate ceiling, that robots.txt is READ and GRADED rather than obeyed, that unreachable targets grade U rather than F, and the exact set of conditions that return errors. This is unusually rich behavioral disclosure for a non-idempotent call.

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, then USE THIS, DO NOT, and COST blocks, so an agent can stop reading early. It is dense and long, and the middle layers breakdown (Access/Indexability/Answerability/Recency with weights) is more detail than a selection decision strictly needs.

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

Completeness5/5

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

Complete for a tool with no output schema and no annotations gap: the description specifies the return format (Markdown under '## Verdict', '## Top issues', '## Fixes'), the grading scale, the U fallback, cost semantics and error conditions. Nothing an agent needs to call and interpret it is missing.

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 the schema already documents the single url parameter thoroughly (formats, fragment/query handling, identity, redirect storage). The prose adds no syntax or format detail beyond it, so the baseline 3 applies.

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?

States a specific verb and resource ('audits ONE live page for AEO/GEO'), names the exact output artifact and grading scale, and explicitly distinguishes itself from lekta_report, lekta_fix_plan and lekta_diff by name. An agent can tell it apart from every sibling without opening a schema.

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

Usage Guidelines5/5

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

Contains explicit USE THIS triggers quoted verbatim ('run an AEO audit', 'why is my page not cited in AI answers') and an explicit DO NOT block naming three alternatives with the condition that selects each. It also states scope limits (one page, not a site) and the 'again after every deploy' re-measure case.

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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