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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

A4.4/5.0
Behavior5/5

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

The description adds behavioral detail beyond annotations: it fetches the page, extracts title/description/key links, and emits standard markdown. It is consistent with the annotations (readOnly, idempotent, non-destructive) and explains the output format.

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

Conciseness3/5

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

The description is two sentences, but the first sentence is long and contains multiple clauses. It front-loads the purpose, but could be more structurally concise.

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?

With no output schema, the description explains the output (single text blob ready for site-root). Parameter count is low, schema covers all, annotations cover safety, and the description is sufficiently complete for the tool's simplicity.

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%, and the description adds context for both parameters: for 'url' it says 'Full URL of the site to summarize', and for 'max_links' it notes 'default 25, max 50'. This adds meaning beyond the schema definitions.

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 (generate llms.txt), resource (any URL), and target users (AI crawlers). It is specific and distinguishes from sibling tools, none of which perform a similar function.

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 lists explicit use cases such as getting a client's site indexed, drafting for your own project, or auditing a competitor. It does not mention when not to use or alternatives, but the context signals show no similar sibling tools, so the guidance is clear enough.

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

A4/5.0
Disambiguation3/5

Tools have distinct purposes but some overlap exists, e.g., multiple ask_pipeworx variants and deep_research could confuse an agent. Prediction market tools are differentiated but not immediately obvious.

Naming Consistency3/5

Names are consistently in snake_case but mix verb and noun orders (e.g., 'ai_visibility_check' vs 'ask_pipeworx'). No strict verb_noun pattern throughout.

Tool Count3/5

34 tools is on the high side but still reasonable given the broad domain coverage. Some tools could be consolidated without loss.

Completeness4/5

Covers data querying, company research, prediction markets, subscriptions, and memory. Minor gaps like no direct web search but ask_pipeworx substitutes.

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