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VincentKaufmann

noapi-google-search-mcp

google_shopping

Search Google Shopping to compare product prices, ratings, stores, and images. Find the best deals on any product.

Instructions

Search Google Shopping for products with prices, stores, ratings, and product images.

Sample prompts that trigger this tool: - "Find the cheapest MacBook Air" - "Compare prices for Sony WH-1000XM5 headphones" - "How much does a Nintendo Switch cost?" - "Search for running shoes under $100" - "Find deals on mechanical keyboards"

Args: query: The product search query string. num_results: Number of results to return (default 5, max 10).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
num_resultsNo
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only describes return content (prices, stores, ratings) but omits authtication needs, rate limits, error responses, or any side effects. For a search tool, this minimal disclosure is insufficient.

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: one-line purpose, then sample prompts, then parameter list. Every sentence adds value, no redundancy. Front-loaded with the core action.

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 tool's simplicity (2 params, no output schema, no annotations), the description covers the purpose, parameters, and typical usage via examples. It mentions return fields (prices, ratings) but could be more explicit about the return format. Still adequate for most use cases.

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 no descriptions (0% coverage), so the description adds crucial meaning: 'query: The product search query string. num_results: Number of results to return (default 5, max 10).' This clarifies both parameters beyond bare type/default information.

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?

Description clearly states 'Search Google Shopping for products with prices, stores, ratings, and product images.' This distinguishes it from sibling tools like google_search (general web) and google_flights (travel), by specifying the resource (Google Shopping) and the product-oriented scope.

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?

Provides explicit sample prompts that trigger the tool, e.g., 'Find the cheapest MacBook Air,' which illustrates typical use cases. However, it does not mention when not to use this tool or suggest alternatives, leaving a slight gap for an AI agent deciding between similar tools.

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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