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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context about external calls and cost ('BYO key — you pay Anthropic directly') and describes the return structure, which annotations don't cover.

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 each add essential information: function/scoring, model/cost options, return format/use cases. No wasted words, and it's front-loaded with the primary verb.

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?

The description compensates for the missing output schema by listing per-model fields and the combined view. It covers model selection and key requirements. Minor gaps remain: 'signals' and 'combined view' are not defined, and there's no edge-case handling.

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 descriptions cover all 4 parameters, so baseline is 3. The description goes beyond the schema by clarifying that the default model is free and that supplying `_apiKey` incurs direct costs to Anthropic, adding operational meaning.

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 clearly identifies the resource (LLMs) and the output (visibility score 0-100). It distinguishes itself from siblings by focusing on measuring AI visibility rather than general Q&A or research.

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?

It explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to add an API key. However, it doesn't mention alternatives or when not to use this tool.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx for general queries, ask_pipeworx_grounded for high-stakes verification, deep_research for multi-faceted research). However, some overlap exists among the ask_* variants and the prediction-market tools (bet_research vs. polymarket_edges vs. polymarket_arbitrage), which could cause misselection without careful reading of the detailed descriptions.

Naming Consistency4/5

Tool names consistently use snake_case and mostly follow the verb_noun pattern (e.g., list_subscriptions, resolve_entity, validate_claim). Minor deviations like random_fact and today_fact (adjective_noun) and pipeworx_feedback (noun_noun) introduce slight inconsistency, but the overall pattern is predictable.

Tool Count3/5

With 33 tools, the count exceeds the typical 3-15 range and even the 16-25 'heavy' threshold. While the server covers an unusually broad domain (data retrieval, prediction markets, memory, subscriptions, AI visibility), several tools could be consolidated (e.g., the six polymarket tools, trivial random_fact/today_fact). The scope partially justifies the count, but it feels over-provisioned.

Completeness4/5

The tool surface is remarkably comprehensive for a data platform: it covers querying, entity resolution, comparison, change feeds, memory persistence, subscription management, validation, and even meta-tool discovery. Minor gaps exist (e.g., no explicit update/delete for external data, but that is not the service's purpose). Overall, no obvious dead ends.

Resources