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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

A4.4/5.0
Behavior4/5

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

Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds valuable context: the default model is free, Anthropic requires the user's own key and direct payment, and the return structure includes per-model scores, confidence, signals, and raw responses. 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 four sentences, each serving a purpose: (1) core function, (2) model and key details, (3) return structure, (4) use cases. No fluff or redundancy.

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?

Given the absence of an output schema, the description adequately covers the return format (per-model object with score, confidence, signals, raw_response, plus combined view). It explains the tool's purpose, parameters, and typical use cases. However, it does not elaborate on what 'signals' or 'confidence' mean, leaving a minor gap.

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%, but the description adds meaning beyond the schema by explaining the default model behavior and the optional nature of the Anthropic key. It also clarifies that 'context' helps disambiguate common names, which the schema alone does not convey.

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 (LLM visibility for a business/brand/product/topic). It distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing on probing multiple LLMs and returning a visibility score.

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 model selection (default Workers AI, optional Anthropic with API key). However, it does not explicitly describe when to avoid using this tool or contrast it directly with alternatives.

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
Disambiguation3/5

Multiple tools answer factual questions (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, validate_claim, deep_research), and ask_pipeworx_beta is explicitly identical to the stable router right now, creating genuine selection ambiguity. Most other clusters—memory, subscriptions, entity research, Polymarket—are reasonably distinct once the verbose descriptions are read.

Naming Consistency4/5

Names are consistently lowercase snake_case with recognizable family prefixes (ask_pipeworx_*, data360_*, polymarket_*, pipeworx_*), which aids grouping. The convention mixes verb-first names like resolve_entity with noun/prefix names like polymarket_edges, but it is still readable and predictable enough.

Tool Count2/5

34 tools is well past the heavy range, and the server bundles several unrelated concerns—universal data routing, prediction-market analytics, memory, subscriptions, AI-visibility marketing, and npm dependency scanning—into one surface. Many tools earn their place, but the aggregate is overloaded and likely to slow tool-selection.

Completeness4/5

The data-research side has strong coverage: discovery, universal routing, grounded verification, entity resolution, profiles, comparisons, recent-changes tracking, and in-record search. Subscription lifecycle and memory are complete, and the prediction-market suite even covers fill-risk and edge persistence; only a few niche read/write operations are absent.

Resources