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web_images

Google Images search for AI agents at $0.010 per call, from the same Serper.dev source as /web/search. Send a query, get back compact JSON: top image results with position, title, image URL, source page link and dimensions. Tune with num (1-10 results), country and language (2-letter codes). Zero results is a valid, honest answer. Pay per call in USDC on Base, no account, no API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numNoNumber of results to return (1-10, default 5)
langNoOptional 2-letter language code for the results, e.g. 'en', 'nl'
queryYesThe search query, plain text, max 400 characters
countryNoOptional 2-letter country code to localise results, e.g. 'us', 'nl'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, but the description fully discloses the tool's behavior: returns compact JSON with image metadata, is read-only, has no authentication or API key, and involves a per-call cost. There are no hidden side effects or destructive actions.

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 concise and well-structured: it states the core function, lists key output fields, mentions tuning parameters, and conveys important operational notes (pricing, no account). Every sentence adds value without redundancy.

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 having no output schema, the description specifies exactly what the output contains (position, title, image URL, source page link, dimensions), and covers all necessary operational context (pricing, payment, no account, no API key). An agent has enough information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The parameter schema has complete descriptions for all four parameters, and the tool description reinforces their meaning (e.g., num controls result count, country/localisation). This provides clear semantic understanding beyond the schema alone.

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 it performs Google Images search, specifies the data source (Serper.dev) and distinguishes it from general web search by noting it returns image results. The purpose is explicit and unambiguous.

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?

It provides concrete usage details: parameters (num, country, language), pricing, and behavioral expectations (zero results is a valid answer). However, it does not explicitly state when to prefer this over alternative tools like web_search or web_contents, though the image-specific nature implies it.

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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