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Build an llms.txt

llms_txt
Read-onlyIdempotent

Build an llms.txt for a site (the llmstxt.org format AI assistants read: the site's name, a one-line summary and its key pages as links with descriptions, grouped by section). Pages come from the sitemap (one language, shallowest first) or the home page's links, each fetched for its title and description; noindex, non-200 and pages canonical to another address are left out. Pass urls to put the pages that matter most first (for example the top pages by clicks from gsc_performance_overview), name or summary to set them. Returns the file and whether the site already has one; it does not write anything.

Input Schema

NameRequiredDescriptionDefault
nameNo
siteYesAny URL on the site.
urlsNoPages to list first, up to 100.
summaryNo
languageNoLanguage prefix to use on a multi-language site, like it or pt-br. Default: the pages without a prefix.
max_pagesNoPages in the file, default 40, max 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description reinforces this with 'it does not write anything' and 'Returns the file and whether the site already has one'. It further discloses filtering rules (noindex, non-200, canonicals to another address excluded) and shallowest-first ordering, which go beyond 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.

Conciseness4/5

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

Two dense sentences that front-load the artifact and format, then sourcing/filtering, then return behavior. No filler, though the parenthetical format explanation and sourcing clauses are packed tightly.

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 still states the return payload (the file plus whether one already exists) and covers sourcing, exclusion rules, ordering, and the no-write guarantee. An agent has everything needed to call it correctly.

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

Parameters4/5

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

At 67% schema coverage the description compensates: it clarifies that `urls` sets priority order and is capped at 100, and that `name`/`summary` override the derived values. It also explains `language` behavior (one language, unprefixed default) and page limits, adding real meaning over the raw schema.

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 artifact ('Build an llms.txt for a site') and immediately names the format standard (llmstxt.org) and its contents. No sibling tool produces this artifact, so it is unambiguous against the catalog.

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

Usage Guidelines4/5

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

Explains where pages are sourced (sitemap or home page links) and recommends passing `urls` to prioritize, citing gsc_performance_overview as a concrete source. It stops short of stating when NOT to use it or naming an alternative tool, but the operational context is clear.

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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