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by io.github.pipeworx-io
OverviewAdmin

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
+  "type": "string"
+}
    • changedInput schema / properties / context / description
      Previous value: -"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."New value: +"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Previous value: -"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."New value: +"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      Added value: +{
+  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description adds rich behavioral context beyond the annotations: it explains the default model (Workers AI free), the optional Anthropic key usage and associated costs (BYO key, pay directly), and details the return structure (per-model score, confidence, signals, raw_response plus combined view). This goes well beyond the readOnly/idempotent hints and provides the agent with actionable expectations.

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 compact and well-structured: first sentence states the core action and output, second sentence explains defaults and optional key, third sentence lists return fields and use cases. Every sentence earns its place with no fluff or repetition.

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?

For a tool with 4 params, no output schema, and moderate complexity, the description is complete: it covers invocation details, API key handling, result format, and use cases. It provides enough context for an agent to select and invoke the tool without further research.

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 extra semantic value by clarifying the default model behavior, the necessity of _apiKey only when Anthropic is included, and in-text examples for entity and context. This enriches the schema descriptions, making the parameters more actionable.

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 explicitly states the tool's function: probing LLMs and scoring visibility. It uses a specific verb+resource ('Probe one or more LLMs... score visibility 0-100 per model') and clearly distinguishes it from siblings by emphasizing per-model visibility scoring and the default model. This makes the purpose unambiguous.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the tool. It does not explicitly mention alternatives or exclusions, but the context is sufficient to infer appropriate usage compared to sibling tools like scan_competitor_ai_presence.

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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