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Photon

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds valuable context: default model cost, API key requirement, and return structure (per-model + combined view). 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?

Two highly informative sentences with no wasted words. First sentence covers main action and output, second addresses optional parameters, third lists use cases. Information is front-loaded and scannable.

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 lacking an output schema, the description explicitly mentions the return format (per-model score, confidence, signals, raw_response + combined view). All 4 parameters are explained. No missing critical information for an agent to use the tool correctly.

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% (all 4 params described). Description enriches by explaining the default model for 'models', the purpose of '_apiKey' (BYO key, direct billing), and how 'context' disambiguates. Adds value beyond the schema.

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... score visibility'), clearly distinguishing from siblings like scan_competitor_ai_presence. It explicitly states the output (score 0-100) and target entities.

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?

Provides clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and context (default free model, optional Anthropic with BYO key). Lacks explicit when-not-to-use or direct sibling differentiation, but the use cases are sufficiently indicative.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, even within clusters like Pipeworx queries (ask_pipeworx vs ask_pipeworx_grounded vs deep_research) and Polymarket tools (bet_research, arbitrage, edges, etc.). Overlap is minimal and explicitly addressed in descriptions.

Naming Consistency5/5

All tool names use snake_case, and most follow a consistent verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity). Exceptions like remember, reverse are still single words in the same style.

Tool Count4/5

32 tools is on the high side but justified by the wide scope: data querying, betting, memory, subscriptions, and utilities. Each tool earns its place, and the count is not excessive given the breadth.

Completeness4/5

The tool set covers the core workflows of querying structured data, comparing entities, validating claims, scanning dependencies, and monitoring, with few gaps (e.g., no direct data visualization). Minor gaps exist but are manageable.

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