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Rentcast

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

A4.3/5.0
Behavior4/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are complemented by description details: default model is free, _apiKey passes through to Anthropic with direct billing, and return format includes per-model fields. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but well-structured: main action first, then default/ApiKey details, return structure, and use cases. It is efficient with no wasted words, though bullet points could improve scannability.

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?

Without an output schema, the description adequately explains the return format (per-model {score, confidence, signals, raw_response} + combined view). Combined with high schema coverage and annotations, it provides sufficient context for correct invocation.

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?

The input schema has 100% coverage with descriptions for all 4 parameters. The description adds value by specifying the default model (Workers AI Llama-3.3-70b, free) and explaining billing implications for _apiKey, plus context for disambiguation.

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 verb 'probe' and clearly states the resource: LLMs for business/brand/product/topic visibility scoring. It uniquely distinguishes from sibling tools like scan_competitor_ai_presence by focusing on AI visibility scores (0-100) per model.

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 provides clear context for use ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the _apiKey parameter for Anthropic. However, it does not explicitly contrast with sibling tools or state when not to use it.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlapping function sets (e.g., multiple Polymarket tools, multiple ask/research tools) could cause confusion. However, descriptions are detailed enough to differentiate.

Naming Consistency4/5

Naming is mostly consistent with snake_case and verb+noun patterns, but a few tools start with nouns (polymarket_*, pipeworx_*), creating minor inconsistency.

Tool Count3/5

At 32 tools, the server feels heavy and covers many disparate domains. While each tool has its place, the high count strains coherence.

Completeness2/5

Given the server name 'Rentcast', only two tools relate to rental data. The rest cover unrelated domains, leaving a major gap for the intended primary purpose.

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