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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.

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds return format details (per-model score, confidence, signals, raw_response + combined view) and clarifies cost model (BYO key for Anthropic). 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?

Three well-structured sentences: action+output, model details, use cases. No filler, every sentence adds value. Front-loaded with core purpose.

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 no output schema, description explains return structure adequately. Covers all 4 parameters, use cases, and behavioral context. Complete for a read-only probing tool with straightforward output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%. Description adds rich context for each parameter: entity examples, model options with default, _apiKey purpose and when required, context disambiguation help. Far exceeds schema basics.

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?

Description clearly states the verb 'probe' and resource 'LLMs for what they know about a business / brand / product / topic' with scoring output. It differentiates from siblings by focusing on brand visibility across models, distinct from general Q&A tools like ask_pipeworx.

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?

Lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Provides model selection guidance (default vs. BYO key). Lacks explicit when-not-to-use or direct comparison to potentially similar siblings 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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TDQS

A3.7/5.0
Disambiguation2/5

The five gads_* tools are distinct, but the majority of the surface is a sprawling research/meta toolkit with many overlapping retrieval entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, validate_claim, entity_profile, compare_entities, recent_changes, and search_within all cover overlapping information-query territory. An agent could easily misroute a question among the ask_pipeworx variants or between the general-query and company-profile tools.

Naming Consistency3/5

Domain prefixes like gads_, polymarket_, and ask_pipeworx_ provide some structure, but naming conventions are mixed: gads_list_campaigns and list_subscriptions follow verb_noun, while entity_profile, ai_visibility_check, remember, and generate_llms_txt do not. The names are readable and grouped by prefix, but they do not form one consistent pattern.

Tool Count2/5

At 36 tools this is a large surface, and the count becomes even more problematic because the server is named Google_ads while only 5 of the 36 tools relate to Google Ads. The other 31 tools are a broad Pipeworx data-research, prediction-market, memory, and subscription utility set, which makes the server feel bloated and mis-scoped for its advertised purpose.

Completeness2/5

As a Google Ads server, the surface is read-only and incomplete: it can list campaigns and ad groups, get campaign details, pull metrics, and run GAQL, but it cannot create, update, or delete campaigns, manage budgets and bids, or handle keywords, audiences, or ad creatives. The many unrelated data-research tools do not address these core Google Ads management gaps.

Resources