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llms.txt checker

dossier_llms_txt
Read-only

Check whether a domain publishes an llms.txt, the markdown index some sites provide for AI agents. Requires a non-HTML content type and a leading markdown heading, so a catch-all page that answers 200 with HTML does not count. One fetch, 10 s timeout. Returns JSON with a status field: {status:"ok", data, fetchedAt} on success, {status:"not_applicable", reason} when the thing is genuinely absent, {status:"timeout", ms}, or {status:"error", message} when it could not be determined. Treat not_applicable as a finding and error as unknown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesPublic domain name, e.g. example.com. IP addresses, ports, paths and protocol prefixes are rejected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • changedInput schema / properties / domain / description
      Previous value: -"Public FQDN, e.g. example.com. Must be resolvable on the public internet; IPs, ports, paths, and protocol prefixes are rejected."New value: +"Public domain name, e.g. example.com. IP addresses, ports, paths and protocol prefixes are rejected."
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

The annotations only declare readOnlyHint and openWorldHint, so the description carries the behavioral burden. It fully discloses the fetch behavior, 10 s timeout, validation criteria, and every status variant including the important guidance to treat not_applicable as a finding and error as unknown.

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 compact, front-loaded with the core purpose, and every sentence earns its place, especially the response format and semantic interpretation. Despite covering multiple statuses and edge cases, it remains easy to parse.

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 fully compensates by enumerating all possible return shapes and their meanings. It also documents the validation criteria and timeout, so an agent has everything needed to call it correctly and interpret the result.

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?

The single parameter is fully documented in the schema, including an example and what is rejected. The description adds no parameter-specific detail beyond what the schema already states, so baseline 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?

States a specific resource (llms.txt), the exact action (check whether a domain publishes it), and clarifies what does not count, namely a catch-all HTML page. This clearly distinguishes it from related dossier tools like dossier_web_surface or dossier_security_txt.

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?

Gives clear context on when the tool is appropriate: verifying llms.txt presence on a domain. It explains the acceptance criteria but does not explicitly name alternatives or state when not to use it, so it stops just short of a 5.

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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