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vparsatwar-git

higgsfield-mcp-unified

generate_video_tool

Submit a video-generation request with a model and prompt to create AI videos. Returns a job handle for polling status.

Instructions

Submit a video-generation request. Returns a job_handle to poll.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
soundNo
promptYes
durationNo
model_idYes
image_urlNo
resolutionNo
end_image_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
backendYes
model_idYes
job_handleYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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 one behavioral trait—that the operation is asynchronous and returns a job_handle to poll—but says nothing about authentication, cost, side effects, or what happens to submitted jobs. This is insufficient for a submission tool with eight parameters.

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

Conciseness4/5

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

Two short sentences, front-loaded with the action and followed by the return behavior. There is no wasted text, though the extreme brevity may under-serve such a parameter-heavy tool.

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

Completeness2/5

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

With no annotations, 0% schema coverage, and eight parameters, the description is far too thin. It mentions the async return but omits parameter semantics, usage context, and behavioral details. The existence of an output schema excuses return-value explanation but not the other gaps.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning for any of the eight parameters. Required fields like model_id and prompt, as well as optional fields like seed, sound, and duration, are left completely unexplained beyond their names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Submit') and resource ('video-generation request'), and mentions the return value ('job_handle'). It distinguishes itself from image or speech siblings by naming video generation, though it doesn't explicitly differentiate from generate_speech_video_tool or generate_batch_tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives like generate_image_tool or generate_speech_video_tool. It only implies usage through the tool name and purpose statement, with no prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.