superhighway-mcp

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image_search

Search multiple image engines, returning direct URLs, source pages, and thumbnails as JSON. Pay per call with USDC microtransactions, no signup or API key needed.

Instructions

Image search. Returns direct image URLs, source pages, and thumbnails as JSON from a multi-engine image metasearch. Paid per call in USDC via x402 — no signup, no API key. Use for visual grounding, finding images for content generation, and multimodal research.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "image_search"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Max results, 1-20 (default 5).
`query`	Yes	The search query.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 5, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses the pricing model (paid per call via x402, no signup, no API key) and the multi-engine metasearch nature. It does not mention rate limits or caching, but for a search tool, the transparency is sufficient.

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 three concise sentences: functional output, pricing model, and use cases. No wasted words; critical information is front-loaded.

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 only 2 parameters and no output schema, the description covers the essentials: what it searches, what it returns (image URLs, source pages, thumbnails, JSON), cost model, and use cases. It is complete enough for an agent to invoke correctly.

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 coverage is 100% both parameters have descriptions. The description adds context about the return format but does not add meaning beyond what the schema already provides for the parameters. Baseline 3 applies.

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 'Image search' and enumerates the outputs (direct image URLs, source pages, thumbnails as JSON). This verb+resource combination is specific and easily distinguishes it from sibling tools like web_search, news_search, and research.

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 explicitly lists use cases: 'visual grounding, finding images for content generation, and multimodal research.' It does not explicitly state when not to use or alternatives, but the sibling tool names imply the context well enough for an AI agent.

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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