Media Generator — Images, Videos & Audios

image_to_video

Input: An image URL and a descriptive prompt detailing the desired video scene.

Output: We will return the generated video URL to the user.

Functionality: This tool converts a static image into a dynamic video. The provided image serves as the base, while the accompanying prompt guides the AI on how to animate the image into a video that reflects the described scene. It leverages the Image-to-Video API to synthesize video content based on these inputs.

Steps:

We will get the user_id from the request context.
We will validate the user's generation tokens.
We will call the Image-to-Video API using the provided image URL and descriptive prompt.
The API processes the inputs and returns a JSON response containing the video URL.
We extract and return the video URL to the user.

INSTRUCTION FOR CLIENT MODEL:

Extract the required input parameters 'image_url' (type: string, URL) and 'prompt' (type: string, describing the desired video scene) from the user's prompt.
Ignore any extraneous information in the user's input.
Pass the extracted values to this tool as 'image_url' and 'prompt'.
Example: For user input "Animate this image https://example.com/image.jpg to show a sunrise over the mountains", extract 'image_url' as 'https://example.com/image.jpg' and 'prompt' as 'sunrise over the mountains'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes
`image_url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses that generation tokens are validated and that an API call is made, which hints at token consumption. It also specifies the output (video URL) and steps. However, it lacks details on failure modes, image URL accessibility requirements, or rate limits, leaving notable gaps.

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 overly verbose with redundant sections: 'Functionality,' 'Steps,' and 'INSTRUCTION FOR CLIENT MODEL' all repeat similar information. It is structured with headings but not concise; several sentences could be merged or removed without losing value.

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?

The description covers inputs, output, internal process, and parameter extraction with an example. An output schema exists, so return values are already structured. Missing error handling and edge cases, but for a two-parameter tool with detailed process steps, it is quite complete.

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?

Schema description coverage is 0%, so the description must compensate. The 'INSTRUCTION FOR CLIENT MODEL' section explicitly defines 'image_url' as a URL string and 'prompt' as a scene description, and provides a concrete example. This adds meaningful semantic meaning beyond the raw schema, though it omits constraints like URL format or prompt length.

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: 'converts a static image into a dynamic video' with the image as base and prompt guiding animation. This specific verb+resource (convert image to video) distinguishes it from siblings like generate_video, which likely creates video from text only.

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?

The description implies when to use the tool (when an image URL and descriptive prompt are available) and gives an example of extraction. However, it does not explicitly compare to alternatives or state exclusions, such as 'use generate_video for text-only prompts.' Usage context is present but not fully elaborated.

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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