Media Generator — Images, Videos & Audios

remove_background

Input: An image URL for background removal.

Output: We will return the image URL with background removed to the user.

Functionality: This tool is used to remove the background from an image. User will provide an image URL and the tool will return the image with the background removed.

Steps:

We will get the user_id from the request context.
We will validate the user's generation tokens.
We will call the Background Remover API with the provided image URL.
This Background Remover API will return a JSON response with the processed image URL.
We will return the processed image URL to the user.

INSTRUCTION FOR CLIENT MODEL:

Extract the required input parameter 'image_url' (type: string, URL) from the user's prompt.
Ignore any extraneous information in the user's input.
Pass the extracted value to this tool as 'image_url'.
Example: For user input "Remove the background from this image https://example.com/image.jpg", extract 'image_url' as 'https://example.com/image.jpg'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`image_url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.8/5.0

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 several behavioral steps: retrieving user_id from context, validating generation tokens, and calling an external Background Remover API. This adds useful context beyond simply 'removes background'. However, it does not mention failure modes, rate limits, or edge cases, leaving room for more transparency.

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 verbose and repetitive. The 'Functionality' section essentially repeats the 'Input' and 'Output' sections, and the 5-step workflow is redundant with the overall function. While the sections are organized, the redundancy makes it less concise than it could be.

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

Completeness3/5

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

For a tool with one parameter and an output schema, the description covers the basic flow: input, output, and mention of token validation. However, it omits potential error scenarios (e.g., invalid URL, API failure) and does not explain the output schema contents, relying on the output schema to fill that gap. It is adequate but not comprehensive.

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

Parameters4/5

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

The input schema only lists image_url as a required string with no description. The description compensates by specifying 'type: string, URL', providing extraction instructions, and giving a concrete example ('https://example.com/image.jpg'). This adds meaningful semantics beyond the schema for the single parameter.

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: 'This tool is used to remove the background from an image.' It specifies the input (image URL) and output (processed image URL), making the action and resource unambiguous. The name 'remove_background' further reinforces the purpose, and it is distinct from sibling tools like 'edit_image_with_text' and 'upscale_image'.

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: user provides an image URL and receives a background-removed image. It also gives an example of how to invoke it. However, it does not explicitly mention when not to use this tool or suggest alternatives, so it lacks exclusion guidance.

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

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