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automatelab-ai-seo

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Generate llms.txt

llms_txt_generate
Read-onlyIdempotent

Reads a domain's sitemap, fetches up to 100 pages, and outputs a spec-compliant llms.txt file with grouped sections. Optionally generates llms-full.txt with full page text.

Instructions

Generate a spec-compliant llms.txt (and optionally llms-full.txt) for a domain by reading its sitemap, sampling up to max_pages pages, and synthesizing a grouped, sectioned summary.

Read-only. Issues one HTTP GET for the sitemap then one per sampled page.

Deterministic; no LLM. Output is the file content as a string - this tool does NOT write to disk or upload anywhere. The caller is responsible for hosting the resulting file at https://<domain>/llms.txt.

When to use: bootstrapping llms.txt for a site you own. To check an existing llms.txt, use llms_txt_validate instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesHostname or origin to generate llms.txt for. Examples: `example.com`, `https://example.com`. The tool reads the domain's sitemap, fetches up to `max_pages` of them, and synthesizes a spec-compliant llms.txt grouped by section. Issues N+1 HTTP GETs: one for the sitemap, then one per sampled page. Read-only.
max_pagesNoHow many pages to sample from the sitemap when building section groupings. Default 30. Each page is fetched (one HTTP GET per page) - keep this low for large sites or rate-limited hosts.
site_nameNoOverride the site name used in the generated llms.txt header. If omitted, inferred from the homepage's <title> tag.
include_fullNoIf true, also generate llms-full.txt (the expanded variant containing full page text, not just URLs and titles). Default false. The llms-full.txt output can be large; only enable when you actually plan to host both files.
site_descriptionNoOverride the site description used in the generated llms.txt header. If omitted, inferred from the homepage's meta description.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes
llms_txtYesThe generated llms.txt file content. Caller is responsible for hosting it.
llms_full_txtNoThe generated llms-full.txt content. Null unless include_full=true.
pages_indexedYesNumber of pages successfully sampled from the sitemap.
Behavior5/5

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

Description adds behavioral details beyond annotations: 'Read-only', 'Issues one HTTP GET for the sitemap then one per sampled page', 'Deterministic; no LLM', 'does NOT write to disk or upload anywhere'. No contradiction with annotations (readOnlyHint=true, idempotentHint=true).

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?

Description is concise, well-structured, and front-loaded with the main action. Every sentence adds value; no redundancy.

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 complexity and presence of output schema, the description fully explains the tool's operation, output format, and caller responsibilities. No gaps.

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 description coverage is 100%, but the description adds meaningful context beyond the schema, e.g., explaining the number of HTTP GETs per page and recommending low max_pages for large sites.

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 generates a spec-compliant llms.txt for a domain by reading its sitemap, sampling pages, and synthesizing a summary. It distinguishes itself from the sibling tool llms_txt_validate.

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 ('bootstrapping llms.txt for a site you own') and when not ('to check an existing llms.txt, use llms_txt_validate instead').

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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