Media Generator — Images, Videos & Audios

generate_image

Input: A textual input in prompt provide by user in the form of image prompt.

Output: We will return the image url output to the user.

Functionality: This tool is used to generate an image based on a text prompt. User will provide a prompt and the tool will generate an image based on the prompt.

Steps:

We will get the user_id from the request context.
We will validate the user's generation tokens.
We will call the Generate Image API with the prompt provided by the user.
This Generate Image API will return a JSON response with the image url.
We will return the image url to the user.

INSTRUCTION FOR CLIENT MODEL:

Extract the required input parameter 'prompt' (type: string) from the user's provided prompt. The prompt should describe the image to generate.
Ignore any extraneous information in the user's input.
Pass the extracted 'prompt' value to this tool as the 'prompt' parameter.
Do not require the user to specify parameter names; extract the value from natural language.
Example: For user input "Generate an image of a cat in a spacesuit on Mars", extract 'cat in a spacesuit on Mars' as the prompt.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It reveals key behaviors such as validating the user's generation tokens, calling the Generate Image API, and returning the image URL. It also outlines a step-by-step process. While it doesn't cover failure modes or edge cases, it provides sufficient transparency for a simple generation tool.

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 well-structured with sections (Input, Output, Functionality, Steps, INSTRUCTION) but is verbose and somewhat repetitive. For instance, 'return the image URL' is mentioned in both the Output section and step 5. While the client instructions are valuable, the description could be tightened to avoid redundancy.

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

Completeness5/5

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

For a tool with a single parameter and no annotations, this description is highly complete. It covers the expected input, output, validation step, and execution steps. The existence of an output schema reduces the need to explain return values, but the description already provides the key outcome (image URL). No critical information is missing.

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?

The input schema only defines 'prompt' as a string with 0% coverage. The description compensates thoroughly by explaining how to extract the prompt from user input, including an example ('cat in a spacesuit on Mars') and instructing to ignore extraneous information. This adds significant meaning beyond the schema.

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 explicitly states the tool's function: 'This tool is used to generate an image based on a text prompt.' It also clearly specifies the output (image URL) and distinguishes itself from sibling tools like generate_video and edit_image_with_text by focusing on text-to-image generation.

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 for when to use the tool: 'User will provide a prompt and the tool will generate an image based on the prompt.' It also includes instructions for the client model on extracting the prompt from natural language. However, it does not explicitly mention alternatives or exclusions relative to sibling tools.

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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