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mcp-for-cascadeur

mcp-for-cascadeur

by Rickcy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

transforms_animate

Destructive

Keyframe object positions and absolute or reference-frame Euler-delta rotations in local or global space with native interpolation for Cascadeur animation.

Instructions

Author positions and absolute or reference-frame Euler-delta rotations, with per-object spaces and native interpolation.

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": {
      "keyframes": "<keyframes>"
    },
    "name": "transforms_animate"
  }
}

Input Schema

NameRequiredDescriptionDefault
keyframesYes
interpolationNo
extend_timelineNo
position_toleranceNoAccepted native position readback error in scene units; does not alter writes.
rotation_toleranceNoAccepted native quaternion angular error in radians; does not alter writes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.6/5.0
Behavior2/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the safety profile is known. The description adds nothing beyond 'Author' about what is overwritten, whether existing keys are replaced or merged, or what happens on partial failure of a 500-keyframe batch. For a destructive batch write, that is a significant gap.

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?

A single front-loaded sentence with no filler. It is dense and slightly buzzwordy ('Author', 'native interpolation'), but every clause carries a concept rather than repeating structured data.

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 destructive tool with a deeply nested keyframe schema, no output schema, and dozens of pose/keyframe siblings, one sentence is not enough. The agent lacks the mutation semantics, frame/timeline context, and verification behavior (the tolerance parameters imply readback checking that the prose never mentions).

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?

With only 40% schema coverage, the description does useful work: 'per-object spaces' maps to the space enum, 'absolute or reference-frame Euler-delta rotations' maps to rotation_wxyz versus rotation_delta_degrees with reference_frame, and 'native interpolation' maps to the interpolation enum. It still says nothing about the required keyframes structure, frame limits, or extend_timeline.

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

Purpose3/5

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

The verb 'Author' plus 'positions and ... rotations' and 'per-object spaces' names the resource, but the description never says the output is timeline keyframes or how it is scoped (scene, timeline, selected objects). It hints at the native-transform niche versus the semantic pose siblings but never draws that line explicitly, so the agent must infer it from jargon.

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 when-to-use guidance and no comparison against the many overlapping siblings (pose_apply, semantic_pose_apply, tracks_set_keys, subtree_pose_apply). Key questions such as 'semantic vs native transforms' or 'keyframe authoring vs single pose apply' are left entirely unanswered.

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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