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detect_ai_image

Analyze images to determine if they were AI-generated, providing a confidence score for classification.

Instructions

Accepts an image file as input and analyzes it to determine the probability that the image was generated by artificial intelligence, providing a confidence score.

Responses:

  • 200 (Success): OK - The request has succeeded, and the image has been classified.

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "data": [
    {
      "probability": 0.9489172697067261,
      "class_name": "not_ai"
    },
    {
      "probability": 0.9489172697067261,
      "class_name": "not_ai"
    }
  ]
}
  • 400: Bad Request - The server could not understand the request due to invalid syntax.

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "message": "message"
}
  • 401: Unauthorized - The client must authenticate itself to get the requested response.

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "message": "message"
}
  • 500: Internal Server Error - The server has encountered a situation it doesn't know how to handle.

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "message": "Internal Server Error"
}
  • 503: Service Unavailable

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "message": "Service Unavailable. Please try again later."
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageNo
Behavior2/5

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions authentication requirements (401 response) and error conditions, but lacks details on rate limits, processing time, model accuracy, or what constitutes a valid image. The HTTP response documentation adds some value but doesn't fully compensate for missing annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is poorly structured with excessive HTTP response documentation that belongs in an output schema. The core purpose is buried under verbose response examples, making it inefficient for quick understanding. The response section adds unnecessary bulk without corresponding value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks crucial details about parameter constraints, performance characteristics, accuracy limitations, and practical usage considerations needed for effective tool invocation.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no information about the single parameter 'image' beyond what's in the schema. It doesn't explain acceptable image formats, size limits, or quality requirements, leaving significant gaps in parameter understanding.

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 the tool's purpose with specific verb ('analyzes') and resource ('image file'), explaining it determines AI generation probability with a confidence score. It distinguishes itself from sibling tools like text_to_image_mystic_sync by focusing on detection rather than generation.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites, limitations, or comparison with other tools, leaving the agent without context for appropriate tool selection.

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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