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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

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive, lowering the bar. The description adds process detail (fetches page, extracts title/description/key links, emits markdown) and output description (single text blob), which goes beyond the structured annotations. 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?

The description is well-structured and front-loaded with the main action. Two sentences plus a useful-for list convey purpose, process, output, and use cases without waste. Every sentence earns its place.

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?

For a simple tool with 2 params and no output schema, the description is highly complete. It covers what, how, output format, and when to use, complementing the annotations and schema. No significant gaps.

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% (both url and max_links have descriptions), so baseline is 3. The description does not add further parameter context beyond the schema; it mentions extraction of key links but doesn't tie it to max_links. No parameters are undocumented, so a 3 is appropriate.

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 states the specific verb+resource: "Generate a production-ready llms.txt file for any URL." It clearly distinguishes from siblings by focusing on outputting the standard llms.txt format, rather than visibility checks or competitor scanning.

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?

Provides a 'Useful for' list with three concrete use cases (client indexing, own project, competitor audit), giving clear context for when to use. However, it does not explicitly mention when not to use or compare with alternatives, so it falls short of the highest bar.

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

A3.9/5.0
Disambiguation3/5

Most tools have clearly differentiated roles, but several pairs blur boundaries: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, and identify vs resolve both wrap the same NCI CACTUS service. The detailed descriptions rescue most selections, but an agent could easily mispick between the research and chemical lookup options.

Naming Consistency3/5

Names are mostly snake_case and readable, but conventions are mixed: some are verb-first (ask_pipeworx, validate_claim, search_within) while many are noun-first or domain-prefixed (entity_profile, polymarket_edges, recent_changes, pipeworx_trending). There is no single predictable pattern for a new tool's name, though subfamilies (polymarket_*, ask_pipeworx_*) are internally consistent.

Tool Count3/5

At 33 tools, this is well above the typical well-scoped range and carries real selection overhead. The unusually broad purpose—a data router plus prediction-market analysis, memory, subscriptions, and several standalone utilities—partially justifies the count, but it still feels heavy and could be consolidated.

Completeness4/5

For its varied subdomains, coverage is strong: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and prediction markets span research, edge scanning, arbitrage, fill-risk, and edge telemetry. Minor gaps exist—such as no direct tool to fetch a specific citation URI by identifier, and the redundant stable/beta router pair—but there are no obvious dead ends.

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