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validate_llms_txt

Validate an llms.txt file against the spec, checking structure, ordering, and link format. Optionally detect broken links via HEAD requests.

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?

Discloses read-only nature, network behavior (HTTP GET for url, zero network for content), optional HEAD requests for link checking, determinism, and that no LLM is involved. Thorough given no annotations are provided.

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?

Three short paragraphs: purpose, behavior, usage guidelines. Every sentence adds value. No repetition or superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, behavior, parameters, and usage context. Lacks explanation of the output format (e.g., returns validation errors/warnings), but given the scope and no output schema, it's still well-rounded.

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 coverage is 100%, but description enhances by explaining use cases (offline validation via content, faster validation by disabling check_links) and clarifying the choice between url and content.

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 clearly states the action ('validate') and resource ('existing llms.txt or llms-full.txt'), specifies what is validated (structure, ordering, link format, broken links), and distinguishes from the sibling tool 'generate_llms_txt'.

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?

Explicitly says 'When to use: auditing an llms.txt you already have' and provides an alternative ('To generate one from scratch, use generate_llms_txt'). Also explains the two input modes (url vs content) and the optional check_links parameter.

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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