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inspect_llms_txt

Fetch and validate a website's llms.txt file to assess its AI visibility. Returns title, summary, sections, links, and structural issues for improvement.

Instructions

Fetch and validate a website's llms.txt file (the Markdown file that tells AI engines what the site is about and which pages matter). Returns its title, summary, sections, links, and any structural issues. Use when asked whether a site has llms.txt or how to improve it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWebsite URL, e.g. https://example.com (llms.txt is derived from it)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states it will fetch and validate the file and return various attributes, but it does not disclose behavior details like HTTP request method, error handling for missing files, or potential side effects. The description provides moderate transparency beyond the schema.

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 consists of two sentences, each serving a distinct purpose: the first explains what the tool does and returns, the second gives usage guidance. No unnecessary words.

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?

Given the tool's simplicity (1 parameter, no output schema), the description adequately covers the purpose, return values, and usage. A minor gap is that it does not explicitly mention behavior when the llms.txt file is missing, but 'structural issues' likely covers that. Overall, it is complete enough for an agent.

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 input schema has 100% coverage for the single parameter 'url', including a description. The tool description adds only a parenthetical note that llms.txt is derived from the URL, which does not significantly augment the schema. 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?

The description clearly states the tool's action ('Fetch and validate') and the specific resource (llms.txt file). It also lists return contents (title, summary, sections, links, structural issues). This distinguishes it from sibling tools like audit_url or check_ai_crawlers, which target different aspects of a site.

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?

The description explicitly says 'Use when asked whether a site has llms.txt or how to improve it,' providing clear context. It does not mention when not to use it or alternatives, but given the focused purpose, it is sufficient.

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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