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Google_maps

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds important behavioral context: default model is free, passing _apiKey enables Anthropic probing with direct billing, and returns per-model fields. 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 the core action, no wasted words. Each sentence earns its place: purpose, model options, return format, and use cases.

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. All 4 parameters are well-documented in schema and description. Use cases are clearly stated, making the tool complete for an agent to decide and invoke.

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 has 100% parameter descriptions, so baseline is 3. The description adds value beyond schema by explaining billing implications for _apiKey, default model selection, and the return format (score, confidence, signals, raw_response). This extra context justifies a 4.

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's purpose: probing LLMs for knowledge about an entity and scoring visibility 0-100. The verb 'probe' and resource 'LLMs' are specific, and the tool is well-differentiated from sibling tools like deep_research or ask_pipeworx by focusing on AI visibility and scoring.

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 usage context: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains default behavior and optional anthropic probing. However, it does not explicitly contrast with sibling tools or state when not to use it, though the unique purpose makes this 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

A3.5/5.0
Disambiguation2/5

The tool set mixes unrelated domains (maps, prediction markets, npm dependencies, memory storage) under one server named 'Google_maps'. While individual tool descriptions are clear, an agent cannot easily distinguish which tools belong to the maps domain and which are extraneous, causing confusion about the server's actual purpose.

Naming Consistency2/5

Tool names lack a consistent convention. Maps tools use 'maps_' prefix, but other tools have names like 'ask_pipeworx', 'bet_research', 'forget', etc., mixing prefixes, verb styles, and underscore usage. No unified naming pattern across the set.

Tool Count2/5

37 tools is excessive for a specialized maps server. Only 7 tools are map-related; the remaining 30 cover disparate domains (financial data, prediction markets, system utilities), making the server seem like a random collection rather than a focused integration.

Completeness2/5

For a maps server, common operations like static map generation, place photos, or timezone lookups are missing. The inclusion of many non-maps tools creates a 'kitchen sink' effect, undermining completeness for the stated purpose. The tool surface is severely incomplete if judged by the server name.

Resources