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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

A4.7/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds critical behavioral details: the probing mechanism, cost implications (BYO key for Anthropic), default model, and output structure. 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?

The description is three sentences with a concise list of output fields, all front-loaded and free of fluff. Every sentence adds essential information (purpose, parameters, return format, use cases).

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?

Despite having no output schema, the description explicitly states the return format (per-model score, confidence, signals, raw_response, combined view). It covers purpose, all parameters, defaults, and use cases, making the tool fully understandable for an AI agent.

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 baseline is 3. The description adds significant value by explaining the default model for the 'models' parameter, how the '_apiKey' is used (passed through to Anthropic), and the purpose of 'context' for disambiguation. This enrichment justifies a score above baseline.

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 clearly identifies the resource (LLMs) and output (visibility score per model). It distinguishes the tool's purpose for AI-marketing audits, brand checks, and competitive monitoring, making it highly clear despite not explicitly differentiating from siblings like 'scan_competitor_ai_presence'.

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 (e.g., 'AI-marketing audits') and clarifies when to use the Anthropic model (requires passing _apiKey). However, it does not mention alternatives or when not to use this tool relative to siblings, leaving some room for ambiguity.

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

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx family (standard, beta, grounded) and validate_claim vs ask_pipeworx_grounded may cause some confusion for agents despite detailed descriptions.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, and most are verb_noun structures (e.g., ask_pipeworx, compare_entities, resolve_entity), making them predictable and easy to distinguish.

Tool Count4/5

With 32 tools, the set is larger than ideal but well-justified by the broad scope of data sources and functionalities (email verification, SEC/FDA lookups, prediction market analysis, memory, subscriptions).

Completeness5/5

The tool set covers the full pipeline from data discovery (discover_tools, suggest_questions) to retrieval, analysis, comparison, verification, and monitoring, with no obvious gaps for its intended use cases.

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