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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, openWorldHint, idempotentHint, destructiveHint. Description adds: costs (free default, BYO key for Anthropic), output structure (per-model {score, confidence, signals, raw_response} + combined view). No contradiction.

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 front-loaded with main action, then details, then use cases. No fluff, every sentence earns its place.

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?

Covers purpose, parameters, output, use cases, and cost. Lacks mention of error handling or full output format, but annotations fill some gaps. Good for a tool with no output schema.

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%. Description adds contextual meaning: entity is the thing to ask about, models are probe targets, _apiKey is for Anthropic, context disambiguates. Goes beyond schema descriptions.

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?

Clearly states verb 'probe' and resource 'LLMs for knowledge about entity', specifies scoring visibility 0-100 per model, and distinguishes from sibling tools (e.g., ask_pipeworx, deep_research) by focusing on brand/topic visibility.

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?

Explicitly mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Provides guidance on default model and API key requirement. Could be improved by mentioning alternatives among siblings.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes despite overlapping domains like prediction markets, but detailed descriptions help agents differentiate. A few tools like `ask_pipeworx` and `deep_research` could be confused without careful reading.

Naming Consistency2/5

Naming patterns are inconsistent, mixing `ask_`, `polymarket_`, `scan_`, `recent_`, `entity_`, etc., with no unifying convention. The server name 'Hash' mismatches the tool set entirely.

Tool Count3/5

32 tools is on the high side for a focused server, but the set covers many areas. The count is slightly above the typical 3-15 range, yet each tool has a clear purpose.

Completeness2/5

The tool set feels like a collection of unrelated utilities rather than a coherent domain. Core hashing functionality is minimal, while other areas like prediction markets are over-represented with gaps elsewhere.

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