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Dart Kr

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining the probing nature, free default model, BYO key for Anthropic, and return format. It does not contradict annotations and provides useful behavioral context beyond 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 moderately concise and well-structured: first sentence states main purpose, then parameter details, then use cases. It could be slightly more terse but is well-organized and front-loaded.

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?

Despite no output schema, the description outlines the return structure (per-model score, confidence, signals, raw_response + combined view). It covers all critical details for a probing tool: entity, optional models, API key handling, and context. No gaps in essential information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema documents all parameters. The description adds meaningful context: default model explanation for 'models' parameter, BYO key clarification for '_apiKey', and usage context for 'context'. This goes 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 uses a specific verb ('Probe') and resource ('LLMs for knowledge about a business/brand/product/topic') and clearly states it scores visibility 0-100. It distinguishes from siblings by focusing on AI visibility scoring, which is unique among the sibling tools.

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 explicitly states when to use the tool: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains default model and optional Anthropic probe. However, it does not explicitly mention when not to use it or compare to alternatives like scan_competitor_ai_presence or entity_profile.

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.

Resources