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Media Generator — Images, Videos & Audios

by ai.saifs

upscale_image

Input: An image URL and an optional scale factor (between 2 and 4) for upscaling.

Output: We will return the upscaled image URL to the user.

Functionality: This tool is used to upscale an image to a higher resolution. User will provide an image URL and optionally a scale factor, and the tool will return an upscaled version of that image.

Steps:

  1. We will get the user_id from the request context.

  2. We will validate the user's generation tokens.

  3. We will call the Image Upscaler API with the provided image URL and scale factor.

  4. This Image Upscaler API will return a JSON response with the upscaled image URL.

  5. We will return the upscaled image URL to the user.

INSTRUCTION FOR CLIENT MODEL:

  • Extract the required input parameter 'image_url' (type: string, URL) and the optional parameter 'scale_factor' (type: integer, default 2, allowed values: 2-4) from the user's prompt.

  • If 'scale_factor' is not specified in the prompt, use the default value 2.

  • Ignore any extraneous information in the user's input.

  • Pass the extracted values to this tool as 'image_url' and 'scale_factor'.

  • Example: For user input "Upscale this image https://example.com/image.jpg by 4x", extract 'image_url' as 'https://example.com/image.jpg' and 'scale_factor' as 4.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlYes
scale_factorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that generation tokens are validated before calling the upscaler API and that it returns a JSON response with the URL. However, it does not state whether the operation is non-destructive, mention rate limits, or describe failure modes. This is moderate transparency but incomplete.

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

Conciseness3/5

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

The description is front-loaded with Input/Output/Functionality, then provides steps and client instructions. It contains redundancy, e.g., 'We will return the upscaled image URL to the user' appears twice, and the steps repeat the main functionality. The 'INSTRUCTION FOR CLIENT MODEL' is useful but adds length. Acceptable structure but not concise.

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 the simple tool (2 params, no annotations, no schema descriptions), the description is fairly complete. It covers the purpose, parameters, return value, and process. The output schema exists, but the description still explains the returned URL. It lacks error handling details but is sufficient for typical usage.

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?

Schema coverage is 0%, but the description fully compensates by defining 'image_url' as a URL string and 'scale_factor' as an integer with default 2 and allowed range 2-4. It also provides an example of how to extract these from user input, which is clear and actionable.

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 'This tool is used to upscale an image to a higher resolution' with explicit input and output (image URL, scale factor, returned upscaled URL). It is distinct from siblings like remove_background or edit_image_with_text by specifying the upscaling action.

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 provides clear context: the user provides an image URL and optional scale factor, and the tool returns an upscaled image. It explains the process steps and gives explicit extraction instructions for the client model, which implies when to use it. No exclusions or alternatives are mentioned, but the purpose is unambiguous.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, such as generate_image, remove_background, and upscale_image. However, logo_generator and generate_image both accept a text prompt and return an image URL, which could cause confusion, and music_generator and song_generator overlap in audio generation, though their inputs differ.

Naming Consistency3/5

Tool names are a mix of verb_noun (generate_image, remove_background), noun_generator (logo_generator, song_generator), and simple verbs (fetch, search, health). While readable, the lack of a single consistent pattern makes it less predictable for an agent.

Tool Count4/5

With 17 tools, the server is slightly above the ideal 3-15 range but not excessively over. Each tool serves a distinct function for generating or manipulating media, so the count feels justified for the broad scope.

Completeness4/5

The toolset covers the core media generation lifecycle: image generation, editing, background removal, upscaling, video generation, image-to-video, music and song generation, and text-to-speech. Minor gaps like video editing or managing past generations exist, but they are not critical for standard workflows.

Resources