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Decodo MCP Server

by Decodo

Google Lens

google_lens
Read-only

Run a Google Lens image search from an image URL and return parsed visual matches as JSON to identify what an image shows or find pages where it appears.

Instructions

Run a Google Lens image search for an image URL through the Decodo Web Scraping API (google_lens target) and return the parsed visual matches as JSON. Use it to identify what an image shows or find pages where it appears. Docs: https://help.decodo.com/docs/web-scraping-api-google-lens

Input Schema

NameRequiredDescriptionDefault
queryYesImage URL for Google Lens search (e.g., "https://example.com/image.jpg")
jsRenderNoShould the request be opened in a headless browser, false by default
deviceTypeNoDevice type to emulate for the request

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, and the description adds real context: the request is proxied through Decodo's Web Scraping API and results come back as parsed JSON. It omits any mention of credentials, quota/rate limits, or latency of the rendering path, so not a full 5.

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?

Two sentences, front-loaded with the action and scope, with the docs link trailing where it belongs. Slightly dense — the API and target-name detail could be trimmed — but every clause carries information.

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?

For a 3-parameter, no-output-schema tool, the description tells the agent the return type (parsed JSON visual matches) and the execution path (Decodo proxy). An agent can call it correctly; only error/auth behavior and result structure detail are absent.

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?

Schema description coverage is 100% with enum and defaults documented, so the baseline is 3. The description only implies that `query` is an image URL and says nothing about `jsRender` or `deviceType` beyond what the schema already states.

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?

States a specific verb (run) and resource (Google Lens image search on an image URL) and names the backend target (`google_lens`). The image-URL dimension clearly separates it from the text-oriented siblings like google_search and search.

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?

Gives two concrete use cases: identify what an image shows, or find pages where it appears. That's clear context, but no explicit when-not or named alternative is offered, so it stops short of the 5 bar.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.