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Comicvine

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, and idempotent. Description adds critical context: external API calls to Anthropic require user-provided key and incur costs, and return format includes per-model details. 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?

Three sentences, front-loaded with purpose, no unnecessary words. Efficiently conveys what, why, and how.

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?

Covers purpose, parameters, and return structure despite no output schema. Lacks error handling or rate limit info, but sufficient for a probe tool with strong annotations.

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?

All 4 parameters are described in the schema (100% coverage). The description adds examples and clarifies the entity parameter type and the optional context parameter's purpose, providing marginal added value.

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 from 0-100. It distinguishes from siblings by focusing on AI visibility measurement rather than general queries or other specific functions.

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?

Description explains typical use cases (AI-marketing audits, pre-launch brand checks) and model selection (default workers-ai, optional anthropic with key). However, it does not explicitly differentiate from similar siblings like 'scan_competitor_ai_presence'.

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 have overlapping purposes, especially the Pipeworx utilities (ask_pipeworx, ask_pipeworx_grounded, deep_research, etc.). The comic tools are distinct but the large number of similar generally-purpose tools creates confusion across the set.

Naming Consistency3/5

Comic tools follow a consistent noun pattern (character, characters, issue, issues) but Pipeworx tools mix verb_phrase (ask_pipeworx), noun_phrase (entity_profile), and composite names (scan_competitor_ai_presence). No single convention dominates.

Tool Count1/5

The server name 'Comicvine' suggests a focused comic book reference, yet 30 of 40 tools are unrelated Pipeworx services (company financials, prediction markets, subscriptions, etc.). This is a severe scope mismatch.

Completeness2/5

The comic-related tools cover characters, issues, volumes, publishers, and creators reasonably well, but the server's overall purpose is diluted by including many non-comic tools that don't form a coherent surface. The comic subset is complete, but the full set is not.

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