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validate_llms_txt

Check llms.txt files for spec compliance: structure, ordering, link format, and optionally detect broken links.

Instructions

Validate an existing llms.txt or llms-full.txt against the spec: structure, section ordering, link format, and (optionally) broken-link detection.

Read-only. One HTTP GET when given url; zero network when given content. Optional link-check issues HEAD requests against each link if check_links is true.

Deterministic; no LLM.

When to use: auditing an llms.txt you already have. To generate one from scratch, use generate_llms_txt.

Either url or content must be provided.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic URL of an existing llms.txt or llms-full.txt to validate (e.g. `https://example.com/llms.txt`). Either this OR `content` is required.
contentNoRaw llms.txt content as a string. Use this to validate a file offline without fetching. Either this OR `url` is required.
check_linksNoIf true (default), HEAD each linked URL to detect broken links. Set false to skip link checks for faster, network-light validation of just the structural rules.
Behavior5/5

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

Details network behavior (one GET for url, zero for content; optional HEAD requests for link checking), confirms read-only and deterministic nature, and states no LLM involvement. Completely compensates for absent 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?

Six sentences deliver purpose, usage, and behavioral details efficiently. No extraneous words; front-loaded with the core action.

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 validation tool with three parameters and no output schema, the description covers what is validated, when to use, network behavior, and parameter constraints. Fully sufficient for an agent to invoke 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?

Schema covers all 3 parameters with descriptions (100%). Description adds mutual-exclusion clarification between url and content, and explains check_links default, providing extra usage context beyond static 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?

Clearly states 'Validate an existing llms.txt or llms-full.txt against the spec', specifying structure, section ordering, link format, and optional link checking. Distinguishes from sibling tool generate_llms_txt by mentioning its purpose for auditing existing files.

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?

Explicit 'When to use' section contrasts with generate_llms_txt, and clarifies the or-relationship between url and content parameters. Provides clear context for tool selection.

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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