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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

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds value by detailing the default model, the need for a BYO API key for Anthropic, and the per-model return structure (score, confidence, signals, raw_response) plus a combined view. No contradictions with annotations.

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 three sentences that front-load the core purpose, then add details in a logical order. Every sentence contributes value without redundancy.

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?

Given the tool's 4 parameters, no output schema, and annotations covering safety, the description fully explains what the tool does, how to use it (default vs. paid models), and what the response contains. An agent has enough information to invoke it correctly.

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 description coverage is 100% for all 4 parameters. The description goes beyond the schema by explaining default behavior (free Workers AI model) and the purpose of the context parameter (disambiguating common names). This adds meaningful context for parameter usage.

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 specific verbs ('probe','score') and clearly identifies the resource (LLMs) and output (visibility score 0-100). It distinguishes itself from sibling tools by focusing on AI visibility measurement for businesses/brands/products/topics.

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 explicitly states use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains the default model and how to use the optional _apiKey for Anthropic. It does not explicitly list when not to use, but the context is clear.

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

Although many tools are individually well-described, there are several overlapping clusters: three ask_pipeworx variants, multiple polymarket edge/arbitrage tools, and AI-visibility checks vs their competitor-comparison wrapper. An agent can easily pick the wrong one because the boundaries (beta vs stable, grounded vs routed, edge vs arbitrage) are subtle despite the verbose descriptions.

Naming Consistency3/5

The set is consistently snake_case and mostly readable, so naming is not chaotic. However, the pattern is mixed: some tools use entur_/polymarket_/pipeworx_ prefixes, others are bare verbs (remember, recall, forget), and some are noun phrases (entity_profile, pipeworx_trending). The ask_pipeworx family also doesn't follow the pipeworx_ prefix convention used by neighboring tools.

Tool Count2/5

34 tools is well past the healthy range for a focused MCP server, and only three tools relate to the Entur transport domain implied by the server name. The other 31 tools form a separate, broad data/prediction-market product that appears bolted on, making the count inappropriate for the apparent purpose.

Completeness2/5

The Entur transport subset has stops search, departures, and journey planning, but misses common public-transport needs such as disruptions, service alerts, and fare/ticket information. The broader tool set is extensive but lacks a single coherent domain to be complete against, leaving the overall surface scattered and hard to trust as an integrated whole.

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