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see-dance-2-prompt-mcp

by gpt-img-2

Build a Seedance-oriented video prompt

build_seedance_prompt
Read-onlyIdempotent

Transform rough ideas into structured video prompts with separate subject and camera motion, reference bindings, and continuity controls for deterministic output in English or Chinese.

Instructions

Turn a rough idea into a deterministic English or Chinese prompt with separate subject and camera motion, exact reference bindings, and continuity controls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ideaYes
avoidNo
lightingNo
workflowNotext-to-video
aspectRatioNo16:9
cameraMotionNo
subjectMotionNo
outputLanguageNoauto
durationSecondsNo
visualTreatmentNo
referenceBindingsNo
Behavior3/5

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

Annotations already cover safety (readOnlyHint, destructiveHint, idempotentHint). The description adds that the output is deterministic and highlights language and structural features, but does not disclose operational behavior such as authentication needs, rate limits, or error handling. Since annotations carry the safety profile, the description adds moderate value but no deep behavioral detail.

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, dense sentence that front-loads the main verb and resource, then lists key features efficiently. It contains no filler and communicates the tool's purpose clearly in a compact form, even though it omits context.

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?

Given 11 parameters, no output schema, and no description of return values, the tool is under-specified. There is no mention of the expected output format (e.g., a string), any specific workflows it belongs to, or how it relates to sibling tools. The description gives a high-level summary but lacks the operational and contextual detail an agent needs for correct use.

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?

The schema has 11 parameters with 0% description coverage, and the description provides almost no parameter-level meaning. It merely implies that 'subject and camera motion' and 'reference bindings' exist as parameters, but does not explain their constraints, formats, or relationships. The description fails to compensate for the lack of schema documentation, leaving an agent without the information needed to populate parameters correctly.

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 states a specific action ('Turn a rough idea into a deterministic prompt') and identifies the resource (video prompt) with key attributes (English or Chinese, separate subject/camera motion, reference bindings, continuity controls). It distinguishes itself from siblings by the core action of building a prompt versus planning, diagnosing, or fetching resources.

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?

No guidance is provided on when to use this tool versus siblings like 'diagnose_seedance_prompt' or 'plan_reference_sequence'. There is no mention of prerequisites, conditions, or exclusions. An agent would have to infer usage from the name alone, without explicit context.

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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