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animate_scenes

Turn chosen scenes of a finished video into moving AI video clips, and render a new animated version of the whole video alongside the original (the original is kept). Multi scene videos only, landscape or vertical; not whiteboard or single scene.

SPENDS the user's animated scenes: one per scene chosen, from their included allowance (Premium 5 a month, Ultimate 10) and any Animation Pack, never their video minutes. State how many it uses and how many they have, and only call it once the user has said which scenes, in their own words. Takes a few minutes; use check_animation for the link. One animation at a time per video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenesYesScene numbers to animate, as numbered by list_video_scenes (starting at 1).
video_idYesThe video's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: it discloses the credit model (spends animated scenes allowance, Premium 5/Ultimate 10 plus Animation Pack, never video minutes), the duration (a few minutes), and the concurrency limit (one animation at a time per video). It also confirms the original video is preserved, which is critical for a mutation tool.

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?

Three sentences, front-loaded with the action and outcome, then constraints, then cost and timing. Dense but slightly repetitive in restating the scene-selection requirement.

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?

Despite no output schema, the description closes the loop by directing the agent to check_animation for the resulting link, and it covers cost, eligibility, concurrency, and sequencing. An agent has everything needed to call this correctly and explain the outcome to the user.

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

Parameters3/5

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

Schema coverage is 100%, so both parameters are already documented, including that scene numbers come from list_video_scenes and start at 1. The description reinforces the 'which scenes, in their own words' framing but adds no format or syntax detail beyond 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?

States a specific verb and resource ('Turn chosen scenes of a finished video into moving AI video clips') plus the side effect of rendering a new animated video alongside the kept original. An agent can distinguish it from siblings like create_video or check_animation without opening a schema.

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?

Explicit when/when-not: multi-scene only, landscape or vertical, not whiteboard or single scene. It also gives the sequencing rule (only call after the user has named the scenes in their own words) and routes the agent to check_animation for the link.

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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