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h3_art_generate

Submit a paid managed H3 text-to-video generation job using a prompt and optional seed, duration, and resolution; requires AUTODL_ART_TOKEN and H3_ART_ENABLE_PAID=1.

Instructions

Submit one paid managed H3 text-to-video task; requires AUTODL_ART_TOKEN and H3_ART_ENABLE_PAID=1.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "h3_art_generate"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNo
promptYes
durationNo
resolutionNo480p横

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the task is paid and requires specific auth/environment configuration, but it does not say whether the call starts an asynchronous job, returns a job ID, blocks, or must be followed by h3_art_job_status.

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 a single front-loaded sentence with no wasted language. Every clause contributes a useful qualifier: paid, managed, single task, and required environment/auth settings.

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?

For a paid generation tool with no annotations, no output schema, and four undocumented parameters, the description is too sparse. It covers task type and environment requirements but omits parameter meanings, job lifecycle/polling behavior, and sibling routing.

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%, so the description must explain all four parameters, but it only implies 'prompt' via 'text-to-video'. It says nothing about seed, duration, or resolution, including the unusual default value '480p横'.

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?

It uses a specific verb, 'Submit', and a specific resource, 'one paid managed H3 text-to-video task'. The 'paid managed' qualifiers help distinguish it from the bare h3_generate sibling, but it does not explicitly name alternatives such as h3_art_generate_workflow or h3_art_quote.

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 states prerequisites (AUTODL_ART_TOKEN and H3_ART_ENABLE_PAID=1), which is useful context. However, it gives no guidance on when to choose this tool over h3_art_generate_workflow, h3_art_quote, or h3_generate, and no when-not conditions.

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