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Dart Kr

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?

Description adds value beyond annotations by detailing the process (fetches page, extracts title/description/key links) and output format. Annotations already declare readOnlyHint, idempotentHint, and non-destructive, and description aligns without contradiction.

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 concise with three sentences plus a bullet list, front-loading the main action and output. Every sentence earns its place with no redundancy.

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 simple operation (2 params, no output schema), the description fully covers what the tool does, how it works, and the output format. No gaps remain for effective usage.

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 baseline is 3. Description briefly mentions max_links defaults (25, max 50) which is already in schema, adding no significant new meaning beyond what the schema provides.

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 purpose: generate a production-ready llms.txt file for any URL. It specifies the output format and distinguishes itself from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on 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?

The description lists concrete use cases (getting client site indexed, drafting own llms.txt, auditing competitor), providing clear context for when to use. It lacks explicit when-not or alternative tool recommendations, but the use cases sufficiently guide selection.

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
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx_beta deliberately matches ask_pipeworx exactly right now, discover_tools and suggest_questions both serve as what-can-I-do entry points, and ai_visibility_check is just the single-entity version of scan_competitor_ai_presence. An agent will struggle to pick the right variant without carefully reading long descriptions.

Naming Consistency3/5

All tools are snake_case and several families share clear prefixes (dart_*, polymarket_*, ask_pipeworx_*), but the overall set mixes verb_noun (discover_tools, validate_claim), noun_phrase (entity_profile, deep_research), bare verbs (remember, recall, forget), and prefix-noun (dart_financials). Readable but not unified.

Tool Count2/5

36 tools is far above the 25+ heavy threshold, and the count is inflated by redundancy: ask_pipeworx_beta is a literal duplicate today, suggest_questions overlaps discover_tools, and ai_visibility_check is subsumed by scan_competitor_ai_presence. The broad Pipeworx platform justifies many tools, but the exposed surface is bloated.

Completeness4/5

The surface covers the apparent domain well: universal querying (ask_pipeworx family + deep_research), tool discovery, entity resolution, profiles, comparisons, change feeds, claim verification, Korean DART filings, Polymarket analysis/fill-risk, memory, subscriptions, and feedback. Minor gaps remain—there's no explicit fetch-by-citation-URI tool despite claims those URIs are fetchable, and no way to retrieve full DART filing text beyond discovery.

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