lekta

Audit a page the way AI answer engines read it

lekta_audit

Read-only

Fetches ONE page exactly the way answer engines do (plain GET as LektaBot + a Chromium render pass) and grades it A+ to F across four layers: Access (robots.txt permissions for ~17 AI crawler tokens), Indexability (how much content exists without JavaScript), Answerability (is there a quotable answer) and Recency (is the page dated). Returns a readable verdict: grade, the issues ranked by point impact, and the complete fix for each. USE THIS when you need the current state of a page or after deploying fixes. DO NOT use it for machine parsing (use lekta_report for JSON), for planning without a new measurement (lekta_fix_plan), or to compare two runs (lekta_diff). COST AND BEHAVIOUR: takes 5-30 s (75 s hard limit), honors robots.txt, consumes one fresh-audit slot from the daily MCP quota; a repeat call within 15 minutes returns the cached measurement for free. The audited site is never modified.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false, and the description reinforces that the site is never modified. It adds substantial context beyond annotations: plain GET as LektaBot plus Chromium render pass, robots.txt honoring, 5-30 s typical latency with 75 s hard limit, per-day quota consumption, and 15-minute caching behavior. There is no contradiction with the annotations.

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 front-loaded with what the tool does and its output, then gives concrete usage and exclusions, then cost/behavior. Every sentence adds operational information an agent needs; there is no filler or redundant restatement of the name.

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?

With no output schema, the description carries the burden of explaining what the agent will receive: a readable verdict with a grade, ranked issues, and complete fixes. It also covers timing, quotas, caching, and robots.txt behavior. For a single-parameter tool, this is sufficient for correct invocation and result interpretation.

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 for the single url parameter is 100%, and the schema already explains that it must be an absolute http(s) URL of ONE page, not a domain or sitemap. The tool description adds little new parameter-level information beyond restating 'ONE page' and 'current state of a page', so the baseline of 3 is appropriate.

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 states a specific action (fetches ONE page as answer engines do) and a concrete outcome (grades A+ to F across Access, Indexability, Answerability, Recency). It explicitly names sibling tools for contrast (lekta_report, lekta_fix_plan, lekta_diff), so the agent can distinguish this tool without opening any schemas.

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 'USE THIS when...' guidance (current state or after deploying fixes) and explicit 'DO NOT use it for...' exclusions with named alternatives. This fully answers when to choose lekta_audit over lekta_report, lekta_fix_plan, or lekta_diff.

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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TDQS

A4.6/5.0

Disambiguation4/5

Most tools have distinct purposes: audit, report, diff, fix_plan, and my_sites are clearly separated. The only potential confusion is between lekta_audit and lekta_report, but their output formats and use cases are explicitly differentiated.

Naming Consistency4/5

All tools share the lekta_ prefix and use lowercase snake_case, which provides strong consistency. The pattern is not strictly verb_noun for every tool (e.g., lekta_my_sites, lekta_report), but these are minor deviations that do not harm readability.

Tool Count5/5

Five tools is well-scoped for the domain of audit tracking and remediation. Each tool serves a necessary step in the workflow without adding redundant or unnecessary surface area.

Completeness5/5

The toolkit covers the full audit lifecycle: performing an audit, retrieving structured results, comparing versions, generating fix plans, and listing tracked sites. No obvious gaps exist for the stated purpose.

Resources

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