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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

A4.3/5.0
Behavior4/5

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

Annotations already mark read-only and idempotent; the description adds key behavioral context: default model is free, Anthropic probing requires a BYO key and incurs direct costs. This clarifies external service usage beyond the annotation hints.

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?

Four sentences, front-loaded with purpose, then default behavior, output shape, and use cases. No filler; every sentence contributes.

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, the description specifies the return structure (per-model fields plus combined view) and practical use cases. Combined with rich annotations, it gives an agent enough to invoke the tool correctly.

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

Parameters3/5

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

Input schema has 100% parameter documentation, so the description doesn't need to re-explain. It does add helpful hints about the default model and key requirement, but these are also present in the schema properties. Thus baseline 3 is appropriate.

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 opens with 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model,' which is a specific verb-action plus resource and output. It clearly distinguishes this from sibling tools by focusing on cross-model visibility scoring.

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?

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') that indicate when to apply it. Does not mention alternatives or exclusions, but the context is clear enough for selection.

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

B3.3/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all query Pipeworx data in similar ways. Polymarket tools also heavily overlap. This leads to ambiguity for an agent trying to select the right tool.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check), all lowercase (feed, forget), and underscore-separated verbs (ask_pipeworx_grounded, list_subscriptions). No consistent pattern is followed.

Tool Count2/5

With 32 tools, the server feels overloaded for its stated purpose of RSS-to-JSON conversion. Many tools are unrelated (e.g., prediction markets, entity profiles, memory) making the scope too broad for a focused server.

Completeness2/5

The tool set has notable gaps: only one RSS-related tool (feed), and missing basic operations like creating or updating entities. The heavy focus on Pipeworx and Polymarket leaves the core domain underserved.

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