Media Generator — Images, Videos & Audios

video_to_audio

Input: A muted video URL along with a textual prompt describing the desired audio.

Output: We will return the video URL with the applied audio.

Functionality: This tool now takes a muted video and a text prompt as input. It generates an audio track based on the provided prompt and applies this audio to the video, resulting in a video with integrated sound.

Steps:

We will get the user_id from the request context.
We will validate the user's generation tokens.
We will call the Audio Application API with the muted video URL and the provided prompt.
The API will generate the audio from the prompt and merge it with the muted video, returning a JSON response with the updated video URL.
We will return the updated video URL to the user.

INSTRUCTION FOR CLIENT MODEL:

Extract the required input parameters 'video_url' (type: string, URL) and 'prompt' (type: string, describing the desired audio) from the user's prompt.
Ignore any extraneous information in the user's input.
Pass the extracted values to this tool as 'video_url' and 'prompt'.
Example: For user input "Add dramatic orchestral music to this video https://example.com/video.mp4", extract 'video_url' as 'https://example.com/video.mp4' and 'prompt' as 'dramatic orchestral music'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes
`video_url`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly describes the internal process: getting user_id from request context, validating generation tokens, calling the Audio Application API, and returning a JSON response with the updated video URL. This gives the agent a clear understanding of side effects and prerequisites.

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

Conciseness5/5

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

The description is well-organized with clear sections (Input, Output, Functionality, Steps, Instructions), and it front-loads the key purpose. Every sentence contributes value, including the practical client-model instructions. Although it's relatively long, the length is justified by the tool's multi-step nature.

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?

The description covers the full workflow from input extraction to output delivery, including token validation and API interaction. It also explains the output format (updated video URL) at a high level, and since an output schema exists, detailed return-value documentation is not necessary. The tool is contextualized well among its siblings despite not explicitly naming alternatives.

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 provides property names and types with zero descriptions. The description adds essential meaning by defining 'video_url' as a URL of a muted video and 'prompt' as a textual description of desired audio, and it includes a concrete example showing how to extract these parameters from user input. This goes far 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 clearly states the tool's function: generating an audio track from a text prompt and applying it to a muted video, returning the video URL. It uses specific action verbs and resource details, distinguishing it from sibling tools like music_generator or text_to_speech that handle standalone audio 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 clearly implies when to use this tool (when a user wants to add AI-generated audio to a video) and provides detailed input requirements. However, it does not explicitly mention alternatives or when-not-to-use scenarios, such as 'for standalone audio generation, use music_generator', so it lacks explicit exclusionary 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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