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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

A4.2/5.0
Behavior4/5

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

Annotations already convey safety (readOnly, idempotent, non-destructive). The description adds valuable behavioral context: the default model is free, Anthropic requires a BYO key with direct billing, and the return format includes per-model fields. No contradictions.

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 very concise (two sentences), front-loads the action and output, and uses clear, direct language. Every sentence adds essential information without redundancy.

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 having no output schema, the description sufficiently describes return values (per-model score, confidence, signals, raw_response + combined view). Use cases are provided. Minor gaps: no mention of pagination or rate limits, but overall complete for a non-complex tool.

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 descriptions cover all parameters (100% coverage), so baseline is 3. The description does not add significant meaning beyond stating the default model and the role of _apiKey, which is already implied in the schema. Minimal added value.

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 specifies the verb 'probe' and the resource 'LLMs', details the output as a visibility score (0-100) per model, and lists concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). This makes the tool's purpose distinct and actionable.

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 contexts for use (AI-marketing audits, pre-launch brand checks, competitive monitoring). However, it does not guide when to avoid this tool or suggest appropriate sibling alternatives like 'scan_competitor_ai_presence' for competitor-specific checks.

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

A4.1/5.0
Disambiguation3/5

There is notable overlap between tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, as well as between polymarket_edges and bet_research. While descriptions differentiate them, an agent may struggle to pick the right one without deep understanding of nuances.

Naming Consistency3/5

Tool names use mixed conventions: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, bet_research), and single-word verbs (forget, recall). Some names are very long (polymarket_fill_risk) while others are terse, reducing predictability.

Tool Count3/5

33 tools is high but can be justified by the broad domain coverage (data retrieval, entity resolution, comparisons, memory, subscriptions). However, several tools serve similar purposes, suggesting potential consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks (SEC, FDA, real estate, prediction markets, etc.) with CRUD-like operations on memory and subscriptions. Minor gaps exist, such as no direct stock trading or social media monitoring, but overall coverage is strong.

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