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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is clear. The description adds that it fetches the page and extracts content, aligning with openWorldHint. No contradictions.

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?

Two focused sentences plus a bulleted use-case list. Every part adds value, and the main action is front-loaded. No waste or redundancy.

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?

Adequate for a simple tool with good annotations and schema. Could mention error handling for unreachable URLs, but overall covers input, process, and output sufficiently.

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 coverage is 100% with descriptions for both url and max_links. The description adds context on what the tool does with these parameters but does not significantly enhance beyond the schema details.

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 an llms.txt file for any URL, specifies the extraction (title/description/key links), and output format (standard markdown). It is distinct from sibling tools like ai_visibility_check.

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?

Lists three explicit use cases (client indexing, own project, competitor audit), providing clear context on when to use. Does not mention exclusions or alternatives, but the use case framing is helpful.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, though the three ask_pipeworx variants (standard, beta, grounded) are very similar, potentially causing confusion. The Polymarket and memory tool families are well-differentiated.

Naming Consistency5/5

All tool names use snake_case consistently, with a clear verb_noun pattern (e.g., resolve_entity, search_datasets, subscribe). No mixing of conventions.

Tool Count4/5

34 tools is high but justified given the breadth of the Pipeworx platform and Ukraine Open Data integration. The set covers a wide range of data sources and operations without feeling bloated.

Completeness4/5

The tool surface is thorough, covering querying, comparison, profiling, subscriptions, and memory. Minor redundancy in ask_pipeworx variants, but no significant gaps for the stated data-access purpose.

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