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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context: default model (Workers AI Llama-3.3-70b free), BYO key for Anthropic with direct payment, and return format (per-model score, confidence, signals, raw_response). No contradiction with annotations.

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?

Three sentences, each providing essential information: action and output, model usage and payment, and return structure. No redundant or extraneous content.

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?

Given 4 parameters and no output schema, the description adequately explains the return structure (per-model object + combined view) and clarifies the optional API key usage. Could mention that output schema is missing but not critical.

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% with all parameters described. The description adds meaning: explains default model behavior, API key purpose for Anthropic, and context parameter for disambiguation.

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 specifies the verb 'probe', the resource 'LLMs', and the output 'score visibility (0-100)'. It clearly distinguishes from siblings like 'ask_pipeworx' by focusing on visibility scoring rather than direct Q&A.

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 states when to use the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It implicitly distinguishes from siblings but does not explicitly mention when not to use or name specific alternatives.

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

Many tools serve overlapping purposes (multiple ask_pipeworx variants, several entity tools, multiple Polymarket edge tools), and while descriptions are detailed, an agent would struggle to quickly select the correct tool without careful reading.

Naming Consistency2/5

Naming conventions are mixed: some use verb_noun (ask_pipeworx, extract_text), others use noun_phrase (ai_visibility_check, bet_research, entity_profile), and no clear pattern dominates.

Tool Count3/5

32 tools is above the typical well-scoped range (3-15). While the broad domain of data query, prediction markets, memory, and subscriptions somewhat justifies the count, it still feels heavy and could benefit from consolidation.

Completeness4/5

The tool set covers most operations for its domain: data query (with multiple depth levels), memory CRUD, subscription lifecycle, and utilities like OCR and dependency scanning. Minor gaps exist (e.g., no direct modify operation), but overall it's fairly complete.

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