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blend-ai

by HoldMyBeer-gg
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Python
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set_interpolation

Change keyframe interpolation on a Blender object property to control animation timing, with options such as CONSTANT, LINEAR, BEZIER, or ELASTIC easing.

Instructions

Set the interpolation type for keyframes on a property.

Args: object_name: Name of the object. data_path: Property data path. interpolation: Interpolation type. One of: CONSTANT, LINEAR, BEZIER, SINE, QUAD, CUBIC, QUART, QUINT, EXPO, CIRC, BACK, BOUNCE, ELASTIC.

Returns: Confirmation dict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_pathYes
object_nameYes
interpolationNoBEZIER

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.7.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / interpolation / enum
      Added value: +[
+  "BACK",
+  "BEZIER",
+  "BOUNCE",
+  "CIRC",
+  "CONSTANT",
+  "CUBIC",
+  "ELASTIC",
+  "EXPO",
+  "LINEAR",
+  "QUAD",
+  "QUART",
+  "QUINT",
+  "SINE"
+]
  2. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Set' implies a mutation, but there is no disclosure of what happens when no keyframes exist, whether it inserts/overwrites, or what side effects it has. It only notes a 'Confirmation dict', which is thin given the output schema already covers returns.

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 purpose sentence followed by compact Args/Returns blocks. Well-sized, with only minor redundancy in restating the enum values already present in the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and 0% schema coverage, the description leaves real gaps: no preconditions, no data_path format, no side-effect notes. The existing output schema and enum reduce the burden on returns and valid values, keeping it at a minimum-viable level.

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 description coverage is 0%, so the description must compensate, and it partially does by naming all three parameters. However, the values are shallow ('Name of the object.', 'Property data path.') and do not explain the data_path format (e.g. 'location' vs a full RNA path), and the interpolation list merely duplicates the schema enum.

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?

States a specific verb and resource: 'Set the interpolation type for keyframes on a property.' An agent can tell it acts on animation keyframe interpolation, not on color ramps or curves. It stops short of explicitly disambiguating from the similar sibling set_color_ramp_interpolation.

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, no prerequisites, no alternatives named. It never states that the object must already have keyframes on the given property, which is the central precondition for this tool to be meaningful.

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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