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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 valuable behavioral context: probing is non-destructive, returns per-model scores and combined view, and explains the cost model (free default, BYO Anthropic key passed through). It does not contradict 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 with no wasted words. Front-loaded with main action, then default behavior, optional key info, return format, and use cases. Every sentence adds value.

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, 100% schema coverage, and no output schema, the description adequately explains the return format (per-model score, confidence, signals, raw_response plus combined view). It covers when to use optional parameters. Not all edge cases (e.g., rate limits) are covered, but it is sufficient for a probe tool.

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 description coverage is 100%, so baseline is 3. The description adds extra meaning by explaining 'entity' as the thing to ask about, 'models' as which models to probe with examples, '_apiKey' as optional and passed straight through, and 'context' for disambiguation. It also clarifies default behavior and cost implications.

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 states the tool probes LLMs for visibility scoring (0-100) per model, with a specific verb 'probe' and resource 'LLMs for business/brand/product/topic'. It differentiates from siblings by focusing on AI visibility scoring, not general Q&A or entity profiling.

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 clear usage context: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to use the optional _apiKey (for Anthropic) and that the default model is free. However, it does not explicitly state when not to use this tool compared to sibling tools like entity_profile or compare_entities.

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
Disambiguation4/5

Most tools have distinct purposes, but the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and multiple Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, etc.) could cause confusion for an agent selecting the appropriate tool.

Naming Consistency2/5

Tool names mix snake_case and camelCase inconsistently, with no strong verb_noun pattern. Examples include 'ask_pipeworx' vs 'discover_tools' vs 'validate_claim', indicating a lack of naming convention.

Tool Count2/5

33 tools is high for a single server, including many utility tools (memory, subscriptions) that seem peripheral to the core regulatory/data domain. This suggests scope creep and could overwhelm agents.

Completeness4/5

The tool set covers a wide range of regulatory and financial data needs, including company profiles, entity comparison, claim validation, FDA catalysts, and prediction market analysis. Minor gaps exist (e.g., no tool for editing data), but overall it is comprehensive for its stated purpose.

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