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提交视频生成任务

seedance_create_video

Submit a single asynchronous Seedance video generation task from a text prompt, returning a task ID and cost metadata for progress polling.

Instructions

Create ONE asynchronous video task, consuming account credits (documented Seedance 2.0 price: 20 credits; confirm current price via list_models). Returns provider task id, statusTask, cost and other metadata immediately. Explicitly defaults to seedance-2.0 / 10s / 9:16 / 720p. Never automatically retry submission on network failure: it may have succeeded. Poll with get_video every 10–20 seconds; failed/interrupted tasks are refunded by the provider.

Input Schema

NameRequiredDescriptionDefault
modelNoEnabled model from list_models. seedance-2.0 supports 10/15s; seedance-2.5-30s supports 30s if available.seedance-2.0
ratioNo9:16
promptYesVideo description. @ImageN must have corresponding reference assets.
durationNoSeconds; explicitly set 30 for seedance-2.5-30s.
reference_asset_idsNoUploaded image IDs in prompt order; seedance-2.0 maximum 8.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare non-read-only, open-world, non-idempotent, non-destructive; the description adds the missing operational context an agent actually needs: credit consumption, the no-retry rule that follows from non-idempotency, provider refunds on failure, and the fact that submission returns immediately rather than blocking.

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?

Front-loaded with the action and cost implication, and no sentence is pure filler. It is a dense run of facts, though, and the pricing clause plus the default-summary clause could be tightened without losing function.

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?

With no output schema, the description still tells the agent what comes back (task id, statusTask, cost, metadata) and how to continue the workflow. For a 5-parameter async creation tool, nothing essential is left for the agent to discover by trial.

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 coverage is 80%, so the schema already carries most parameter meaning; the description still adds value by naming the concrete defaults (seedance-2.0 / 10s / 9:16 / 720p) and pointing at the duration/model coupling. The 720p default is not represented in the schema, which is genuinely extra information.

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?

States a specific verb and resource (create ONE asynchronous video task) plus the key side effect (consumes account credits) and the immediate return payload (provider task id, statusTask, cost). It is unmistakably distinct from read-only siblings like get_video or list_models.

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

Usage Guidelines5/5

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

Gives explicit routing and sequencing: poll with get_video every 10–20 seconds, never auto-retry on network failure, and confirm pricing via list_models. Both when-to-use and when-not-to-do-something are spelled out.

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