Media Generator — Images, Videos & Audios

generate_video

Input: A textual input in prompt provided by user describing the video scene to be generated.

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

Functionality: This tool is used to generate a video based on a text prompt. User will provide a detailed description of the scene and the tool will generate a video based on that description.

Steps:

We will get the user_id from the request context.
We will validate the user's generation tokens.
We will call the Text to Video API with the prompt provided by the user.
This Text to Video API will return a JSON response with the video details including URL.
We will return the video 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 video scene to generate.
Ignore any extraneous information in the user's input.
Pass the extracted 'prompt' value to this tool as the 'prompt' parameter.
Example: For user input "Generate a video of a robot dancing in a city at night", extract 'robot dancing in a city at night' as the prompt.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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 internal steps: validating user's generation tokens, calling the Text to Video API, and returning a URL. This adds meaningful behavioral context beyond the schema, though error cases and failure handling are not mentioned.

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 verbose and redundant (e.g., 'generate a video based on a text prompt' stated multiple times). It is well-structured with sections and numbered steps, but could be trimmed to be more 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 moderate complexity and the presence of an output schema, the description covers the input extraction, token validation, API call, and return value. It lacks error-handling details but is otherwise complete for an agent to invoke the tool correctly.

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 explicitly explains the 'prompt' parameter, including type, extraction instructions, and a concrete example. This fully compensates for the lack of parameter details in 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 generates a video from a text prompt, with a specific verb ('generate') and resource ('video'). It distinguishes itself from siblings like generate_image and image_to_video by focusing on text-to-video 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 indicates the tool is used when the user provides a textual description of a video scene. It does not explicitly compare with sibling tools or state when-not-to-use, but the context is unambiguous enough for tool selection.

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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