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Searchapi

by io.searchapi
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Google Lens

google_lens
Read-only

Analyze images using Google Lens. Upload an image URL to get visual matches, web results, product identification, text extraction, and exact match detection. Supports refining results with a text query for search types: all and visual_matches.

Required: url (a publicly accessible image URL).

Input Schema

NameRequiredDescriptionDefault
qNoOptional text query to refine image search results. Only works with search_type: all or visual_matches.
urlYesPublic URL of the image to analyze. Required.
cropNoCrop region as left;top;right;bottom with normalized 0-1 coordinates (e.g., '0.1;0.2;0.6;0.8'). Searches only the specified region of the image.
countryNoCountry code for localized results (e.g., 'us', 'gb'). Uses ISO 3166-1 alpha-2 format.
safe_searchNoSafe search filtering. 'active' enables strict filtering, 'blur' blurs explicit content (default), 'off' disables filtering.
search_typeNoType of lens analysis. 'all' for general analysis, 'visual_matches' for similar images (some carry price and stock), 'exact_matches' for identical images. Default: all.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / q / description
      Previous value: -"Optional text query to refine image search results. Only works with search_type: all, visual_matches, or products."New value: +"Optional text query to refine image search results. Only works with search_type: all or visual_matches."
    • changedInput schema / properties / search_type / description
      Previous value: -"Type of lens analysis. 'all' for general analysis, 'visual_matches' for similar images, 'products' for shopping, 'exact_matches' for identical images. Default: all."New value: +"Type of lens analysis. 'all' for general analysis, 'visual_matches' for similar images (some carry price and stock), 'exact_matches' for identical images. Default: all."
    • changedInput schema / properties / search_type / enum
      Previous value: -[
-  "all",
-  "visual_matches",
-  "products",
-  "exact_matches"
-]New value: +[
+  "all",
+  "visual_matches",
+  "exact_matches"
+]
  2. Changed1 schema field changed
    • addedInput schema / $schema
      Added value: +"https://json-schema.org/draft/2020-12/schema"
  3. Changed2 schema fields changed
    • changedInput schema / properties / safe_search / description
      Previous value: -"Safe search filtering. 'blur' blurs explicit content (default), 'off' disables filtering."New value: +"Safe search filtering. 'active' enables strict filtering, 'blur' blurs explicit content (default), 'off' disables filtering."
    • changedInput schema / properties / safe_search / enum
      Previous value: -[
-  "blur",
-  "off"
-]New value: +[
+  "active",
+  "blur",
+  "off"
+]
  4. Changed1 schema field changed
    • addedInput schema / properties / crop
      Added value: +{
+  "description": "Crop region as left;top;right;bottom with normalized 0-1 coordinates (e.g., '0.1;0.2;0.6;0.8'). Searches only the specified region of the image.",
+  "type": "string"
+}
  5. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds real value beyond them by disclosing the kinds of results returned (matches, web results, product ID, OCR-style text extraction) and the constraint that q only works with certain search types.

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?

Front-loads purpose, then capabilities, then the query-refinement caveat. Efficient overall, though the trailing 'Required: url' sentence duplicates what the schema already marks as required and does not earn its place.

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?

With no output schema, the description must carry the return-value burden, and it does so by naming the result categories. Combined with 100% schema coverage for the six parameters, an agent has enough to call the tool correctly; only result format/pagination detail is absent, which is minor for an open-world read tool.

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%, so all six parameters, both enums, and the crop format are already documented in the schema. The description only restates that url is required and repeats the q/search_type interaction, adding little beyond the structured fields. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (analyze) and resource (images via Google Lens), and enumerates the concrete outputs: visual matches, web results, product identification, text extraction, exact match detection. Clear on its own, but never contrasts itself with the closest sibling, google_images_light, leaving the agent to infer the difference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage through the capability list and notes that the text-query refinement only applies to search_type 'all' or 'visual_matches', which is a genuine usage constraint. However, there is no explicit when-to-use-this vs google_images_light or google_search_light guidance, and no prerequisites beyond the required url.

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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