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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, read, quote and cite 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 36 published AI crawler tokens: 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", "will ChatGPT / Claude / Perplexity quote this page", "check my robots.txt for AI crawlers" — and again after every deploy to re-measure. 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

A5/5.0
Behavior5/5

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

Annotations already provide readOnly, openWorld, non-idempotent, and non-destructive hints, and the description goes well beyond them by explaining the practical consequences: it reads and grades robots.txt rather than obeying it, is a blocking call with a 75s hard limit, can replay cached results within 15 minutes, consumes daily quota on fresh runs, returns U instead of F for unreachable targets, and lists the exact error conditions. None of this contradicts the annotations; it enriches them substantially.

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?

The description is long, but the tool is genuinely complex and the length is justified by dense, non-redundant content. It is well structured with scannable sections for usage, exclusions, cost, and behavior, and the most important facts (what it audits, how it grades, and what it returns) are front-loaded. Every chunk earns its place.

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?

For a single-parameter tool with no output schema, the description is unusually complete. It specifies the exact Markdown return structure, the grading layers and weights, the cap on unmeasured layers, the quota and caching rules, and the full set of error outcomes. An agent has everything needed to decide when to call it and to interpret the result.

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

Parameters5/5

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

Even though schema description coverage is 100%, the description adds important behavioral meaning to the single url parameter: normalization rules (http upgrade, fragment drop, hostname lower-casing, query string identity), redirect handling, and the critical distinction between the final measured URL and the stored key. This extra context helps an agent pass the same URL to lekta_fix_plan and lekta_diff correctly.

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?

The description opens with a specific verb and resource: 'Audits ONE live page for AEO (Answer Engine Optimization), also called GEO.' It names the grading scale (A+ to F) and the four weighted layers, and explicitly differentiates itself from siblings like lekta_report, lekta_fix_plan, and lekta_diff. An agent can immediately tell what this tool does and what it is not.

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?

The description gives explicit triggers ('USE THIS for "run an AEO audit", ...') and explicit exclusions ('DO NOT use it when code will parse the result', 'to compare two runs', etc.). It also states the scope boundary — one page, not a site — and clarifies when the same URL string must be passed to sibling tools. This is model usage guidance.

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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