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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
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds that the tool makes external API calls to LLMs, requires a personal API key for Anthropic, and returns detailed per-model results. 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?

Description is concise (4 sentences), front-loaded with main purpose, and every sentence adds 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?

Despite no output schema, the description explains return structure per-model and combined view. Covers parameter usage and use cases adequately for a 4-param tool with simple semantics.

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?

Schema coverage is 100%, so baseline is 3. Description adds minor usage context (default model, why context is useful) but does not significantly extend schema documentation.

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 clearly states the tool probes LLMs for knowledge about an entity and scores visibility 0-100, using specific verbs and resources. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general brand visibility rather than competitive analysis.

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?

Explicitly mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Also explains when to use default model vs. Anthropic with API key. Does not list alternatives or when not to use, but 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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and validate_claim all perform data lookups with subtle differences. Polymarket tools also have overlapping scopes. The large number of tools with similar functions creates confusion.

Naming Consistency3/5

Names are inconsistent: some use verb_noun (get_image, list_subscriptions), others are descriptive phrases (ai_visibility_check, bet_research), and some are single words (forget, recall). No clear pattern.

Tool Count2/5

33 tools is high, and many are unrelated to Dockerhub. The server name suggests a focused Docker toolset, but the bulk of tools are for Pipeworx/Polymarket/data lookups, making the count excessive for the advertised domain.

Completeness2/5

As a Dockerhub server, it lacks basic Docker operations like push, delete, or manage repositories. As a general data toolset, it covers many domains but still misses some core operations (e.g., no tool for searching inside images).

Resources