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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 indicate safe, idempotent, non-destructive behavior. Description adds that it fetches the page and extracts content, revealing external network calls, which annotations don't cover. 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.

Conciseness4/5

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

Single paragraph, front-loaded with main action, informative yet efficient. No wasted sentences; covers purpose, process, and use cases.

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 annotations and 2 params, description explains process, output format, and use cases adequately. Lacks error handling or rate limits, but for a simple tool this is sufficient.

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 clear descriptions for both parameters. The description does not add significant new meaning beyond the schema, so baseline 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?

Description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the output format and use cases. It distinguishes from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on standard llms.txt generation.

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?

Explicitly lists useful scenarios (getting site indexed, drafting llms.txt, auditing competitors). However, it does not provide explicit when-not-to-use or alternative tools, though siblings imply differentiation.

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

The tool set includes several overlapping research/lookup tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) and multiple Polymarket tools, which could confuse an agent despite detailed descriptions. The boundaries between these tools are explained in the descriptions, but the sheer number of similar-purpose tools creates ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., search_crates, get_versions, validate_claim), but there are exceptions like entity_profile, deep_research, and bet_research, which break the pattern. The overall naming is readable and mostly consistent, with the polymarket_ and pipeworx_ prefixes providing grouping.

Tool Count2/5

With 35 tools, the server is far too heavy for a focused service. The server name 'Crates' suggests a narrow domain, but only 5 tools relate to Rust crates, while the rest cover disparate areas (Pipeworx, Polymarket, memory management). This mismatch and high count make the tool surface unwieldy.

Completeness4/5

For the underlying Pipeworx/Polymarket domain that the majority of tools serve, the coverage is strong: lookup, grounded answers, research, entity profiles, comparisons, changes, claim validation, scanning, memory, subscriptions, tool discovery. Minor gaps exist (e.g., no direct summarization), but the set feels well-rounded for a data analysis agent.

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