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by io.github.pipeworx-io

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and outputs a single text blob. This goes beyond the annotation basics and explains the processing pipeline.

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 three sentences, front-loaded with the core action, and each sentence earns its place. The 'Useful for' list adds practical context without being bloated. It's slightly list-heavy but overall efficient and well-structured.

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 (2 params, no output schema), the description is sufficiently complete: it explains what the tool does, the output format, and when to use it. The mention of 'single text blob ready to drop at site-root/llms.txt' partially compensates for the missing output schema. No major gaps remain.

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?

Schema description coverage is 100% for both parameters, so the schema already documents url and max_links fully. The description does not add detail about parameter syntax or semantics beyond what the schema provides, but it does mention 'key links' which aligns with max_links. This is a solid baseline score.

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 a specific verb ('Generate') and resource ('a production-ready llms.txt file'), and explains the process (fetches, extracts, emits). It distinguishes itself from sibling tools focused on visibility checking by centering on file generation, making the 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 description offers explicit use cases ('getting a client's site indexed', 'drafting llms.txt for your own project', 'auditing how an AI crawler would see a competitor'), giving clear context on when to use it. It doesn't name alternative tools, but the use-case list effectively guides the agent.

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
Disambiguation2/5

Several tools have significantly overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform data retrieval, with beta currently identical to stable. The polymarket_* family and bet_research also blur boundaries, and discover_tools vs suggest_questions both handle discovery. Despite detailed descriptions, agents are likely to misselect among these overlapping options.

Naming Consistency3/5

All tools use snake_case, but conventions are mixed: some start with verbs (get_launch, search_launches, ask_pipeworx), others are noun phrases (entity_profile, pipeworx_trending), and there are versioned suffixes (ask_pipeworx_beta). While each domain group has internal consistency, the overall set lacks a unified pattern.

Tool Count2/5

35 tools is excessive for a server named 'launches'—only 4 tools (get_launch, get_past_launches, get_upcoming_launches, search_launches) actually relate to space launches. The remaining 31 tools cover unrelated domains like prediction markets, company profiles, and memory, making the count inappropriate and diluting the server's focus.

Completeness2/5

For the claimed launch domain, the set provides only basic list/detail/search operations and lacks useful launch features like filtering by agency, date range, or launch site. More critically, the inclusion of 31 unrelated tools creates a fragmented surface with obvious dead ends—an agent expecting a launch-focused server would find most tools irrelevant.

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