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Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

TDQS

A4.1/5.0
Behavior4/5

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

The annotations already declare readOnlyHint and idempotentHint, but the description adds valuable behavioral context: it explains the process (fetch, extract title/description/links) and the output format (single text blob ready for site-root). This goes beyond the annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by process and output details. The 'Useful for' list extends length but is structured and earns its place by aiding usage guidance. No fluff, though it could be tightened by one less example without loss.

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?

For a tool with two parameters and no output schema, the description covers the input, process, output format, and use cases. It is complete enough for an agent to select and invoke the tool correctly. Minor omissions like error handling or edge cases are not critical for this simple fetch-and-generate operation.

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 schema description coverage is 100%, so the parameters url and max_links are fully explained in the input schema. The description does not add significant additional meaning about the parameters themselves, but it does imply the url is the primary input. Baseline 3 is appropriate given the high schema coverage.

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 uses a specific verb ('generate') and resource ('llms.txt file') and clearly explains what it does: fetches a URL, extracts metadata, and outputs standard llms.txt format. Sibling tools like 'recall' or 'entity_profile' serve distinct purposes, making this tool's purpose unmistakable.

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 'Useful for' section provides concrete scenarios (client indexing, personal project drafting, competitor auditing), which clearly implies when to use the tool. It does not explicitly state alternatives or exclusions, but the sibling list contains no competing tool for this task, so the guidance 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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TDQS

A3.7/5.0
Disambiguation3/5

The tool set mixes two unrelated domains (Star Wars and Pipeworx data services), which is initially confusing. Within the Pipeworx suite, tools like ask_pipeworx and ask_pipeworx_grounded are clearly differentiated, but some overlap exists (e.g., deep_research vs. compare_entities both do multi-source lookups). Overall, most tools have distinct purposes, but the domain mismatch lowers clarity.

Naming Consistency4/5

All tools use snake_case consistently (e.g., ask_pipeworx, entity_profile, resolve_entity). The naming pattern is mostly verb_noun or descriptive_compound, which is predictable. Minor deviation: some tools start with a verb (ask_pipeworx) while others start with a noun (entity_profile), but the style is uniform.

Tool Count3/5

34 tools is on the high side but not unreasonable for a data-heavy server. However, the set covers two distinct domains (Star Wars and Pipeworx), making it feel bloated. The count could be reduced by separating the domains or pruning rarely-used tools.

Completeness3/5

The Pipeworx side appears comprehensive, covering queries, profiles, comparisons, subscriptions, alerts, and memory. The Star Wars side is incomplete—it lacks tools for vehicles, species, or individual characters (only search_people exists). The overall surface has gaps in one of its two domains.