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Darshan972

Scrapingdog MCP Server

by Darshan972

Google Lens API

google_lens

Perform reverse-image search with Google Lens to find visual matches and product results. Use an image URL and optional filters like country, language, and product mode.

Instructions

Reverse-image search via Google Lens for visual matches and products.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the image to run through Google Lens.
queryNoOptional text query to run alongside the image.
countryNoTwo-letter ISO country code to geo-target results (e.g. us, gb, in, de). (API default: us)
productNoEnable product results. (API default: false)
languageNoResult language code (e.g. en, es, fr, de). (API default: en)
exact_matchesNoEnable exact-match results. (API default: false)
visual_matchesNoEnable visual-match results. (API default: false)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the function and says nothing about output format, rate limits, authentication, or result structure. This is a significant gap for a tool with no annotation support.

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?

The description is a single, front-loaded sentence with zero wasted words. It immediately communicates the core action and the specific result types, making it highly scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although the schema is thorough, the tool has 7 parameters, no output schema, and no annotations. The description does not explain what the response looks like, how to choose between result types, or any behavioral nuances. This makes it insufficient for fully understanding the tool's context.

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?

The input schema documents all 7 parameters with descriptions, achieving 100% coverage, so the baseline is 3. The description adds little beyond the schema—only mentioning 'visual matches and products' which maps to existing schema fields, but does not enrich understanding of the parameters.

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 a specific verb ('Reverse-image search') and resource ('via Google Lens'), and explicitly states the scope ('for visual matches and products'). This clearly distinguishes it from siblings like google_search or google_images.

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 clearly implies the tool is for reverse-image search and mentions two output categories (visual matches, products), giving clear context. However, it does not explicitly mention when not to use it or name alternative tools, so it stops one step short of a full 5.

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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