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Ieee Standards

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 indicate safe, idempotent, non-destructive behavior. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and outputs a text blob in standard llms.txt format. This goes beyond annotations without contradicting them.

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 concise, front-loading the main purpose and use cases. It is a single paragraph but could be slightly better structured for skimming, yet it remains efficient without wasted words.

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?

Given the low complexity (2 params, no output schema, no enums, fully documented schema), the description provides complete context: what it does, how it works (fetch, extract, output), and when to use it. No critical information is missing.

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%, so both parameters are already documented. The description does not add new semantic information beyond the schema, so the baseline score of 3 applies.

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 uses a specific verb ('Generate') and resource ('llms.txt file') and clearly states the tool's purpose. It distinguishes itself from sibling tools by focusing on a specific output format and use case for AI crawlers.

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 provides explicit use cases ('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'), giving clear context for when to use it. It doesn't include exclusions or alternatives, but the sibling list shows no direct alternative.

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

A3.8/5.0
Disambiguation2/5

Several tools blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions with overlapping routing behavior, while polymarket_edges, polymarket_arbitrage, and bet_research all surface prediction-market opportunities. Long descriptions help, but the boundaries between these clusters are genuinely unclear, and the three IEEE tools are buried in a sea of unrelated Pipeworx tools.

Naming Consistency4/5

The naming is predominantly snake_case with clear family prefixes like ieee_, ask_pipeworx_, and polymarket_, and most tools follow a readable verb_noun or noun_verb shape. Minor deviations exist (remember/recall/forget, bet_research, pipeworx_trending) but there is no camelCase/mixed-convention problem, so the overall pattern is predictable.

Tool Count1/5

A server named 'Ieee Standards' exposes 34 tools, yet only three of them (ieee_search, ieee_standard_search, ieee_article) actually serve that domain. The remaining 31 tools form a general Pipeworx data, prediction-market, memory, and subscription platform, which is an extreme scope mismatch for the stated server purpose.

Completeness4/5

For the IEEE lookup domain, the three IEEE tools cover the core read-only workflow: broad corpus search, standards-specific search, and full metadata retrieval by article number or DOI. Minor gaps exist (browsing by committee, revision/status history, full-text access) but those are workable or inherently restricted by IEEE's paywall.

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