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AirGallery

product_studio

Turn a product photo into a styled e-commerce shot. Returns { processedImage }. Cost: 3 credits.

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": {
      "image": "<image>"
    },
    "name": "product_studio"
  }
}

Input Schema

NameRequiredDescriptionDefault
imageYesImage as a base64 data URI (data:image/png;base64,…) or an HTTPS image URL
backgroundNoScene (default white_studio)
customPromptNoOptional custom scene description

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose two things annotations cannot: a fixed cost of 3 credits and the return shape { processedImage }. It does not say whether the operation is synchronous, whether the original image is preserved, what resolution or aspect ratio comes back, or what happens when customPrompt and background conflict.

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?

Three short sentences with zero filler, and the core action is front-loaded before the return value and cost. Every sentence carries distinct 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, non-nested tool with full schema coverage, the description covers the action, the return key, and the credit cost, which is enough to invoke it correctly. Minor gaps remain around input size limits and how background interacts with customPrompt, and there is no output schema to fall back on for the response shape.

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%, including a documented enum for background and the base64-or-URL format for image, so the schema already does the parameter work. The description adds nothing about parameter behavior, such as whether customPrompt overrides background. 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 and resource pair: turns a product photo into a styled e-commerce shot. That is a clear transformation, and the e-commerce framing implicitly separates it from portrait/avatar/style-transfer siblings. It stops short of naming a specific alternative, so it is not a 5.

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?

Usage is only implied by the phrase 'product photo' and 'e-commerce shot'; an agent can infer this is for commercial product imagery rather than portraits or artistic restyling. There is no explicit when-not condition, no prerequisites, and no named alternative such as dslr_transform or enhance_image for other photo-cleanup needs.

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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