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Validate llms.txt

llms_txt_validate
Read-onlyIdempotent

Validate an existing llms.txt file against the spec: check structure, section ordering, link format, and optionally detect broken links. Input a URL or raw content.

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 llms_txt_generate.

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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
validYesWhether the file passes structural and link rules.
sourceYes
findingsYes
Behavior5/5

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

The description adds key behavioral traits: '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.' This goes beyond the annotations which already declare readOnlyHint and idempotentHint.

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?

Five sentences, each conveying essential information. No redundant phrases. Front-loaded with the core purpose.

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?

Given the presence of an output schema (not shown but context indicates), the description does not need to detail return values. It covers usage, network behavior, constraints, and alternatives comprehensively.

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%, so baseline is 3. The description adds value by clarifying the mutual exclusivity of url and content, and the behavior of check_links (default true).

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 specifies 'Validate an existing llms.txt or llms-full.txt against the spec: structure, section ordering, link format, and (optionally) broken-link detection.' It clearly distinguishes from the sibling tool llms_txt_generate, which is for generation.

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 states 'When to use: auditing an llms.txt you already have. To generate one from scratch, use llms_txt_generate.' Also clarifies the requirement that 'Either url or content must be provided.'

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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