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blend-ai

by HoldMyBeer-gg
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Python
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insert_keyframe

Add a keyframe to a Blender object property at a chosen frame, optionally setting its value first, to animate location, rotation, scale, or indexed channels.

Instructions

Insert a keyframe on an object property at a specific frame.

Args: object_name: Name of the object. data_path: Property to keyframe. Must be one of: location, rotation_euler, rotation_quaternion, scale, or indexed variants like location[0]. frame: Frame number to insert the keyframe at. value: Optional value to set before inserting the keyframe: a number for an indexed path like location[2], a list for a whole vector.

Returns: Confirmation dict with keyframe details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
frameYes
valueNo
data_pathYes
object_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.0.1
    • addedInput schema / properties / value / anyOf
      Added value: +[
+  {
+    "type": "number"
+  },
+  {
+    "items": {
+      "type": "number"
+    },
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
  2. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that an optional value is set before inserting the keyframe and that a confirmation dict is returned, but it omits whether existing keyframes are overwritten, what prerequisites exist, or how the operation affects animation data.

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?

The purpose is front-loaded in the first sentence, followed by a clean Args/Returns structure. Every line adds useful information without excess, though the format is conventional rather than exceptionally tight.

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?

The description covers the basic operation and parameter semantics, and an output schema exists so return values need not be fully explained. However, for a mutation tool with no annotations, it lacks prerequisites, side-effect details, and when-to-use guidance, leaving clear gaps.

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 description coverage is 0%, so the description must compensate. It does so well by documenting all four parameters, including the allowed data_path values and variants, the meaning of frame, and the accepted value formats (number or list). It still does not specify object_name constraints or frame range.

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 (insert), resource (keyframe), and scope (on an object property at a specific frame). The verb distinguishes it from siblings like delete_keyframe and list_keyframes, but the description does not explicitly name or contrast those alternatives.

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?

Provides no guidance on when to use this tool versus alternatives such as set_frame, delete_keyframe, or list_keyframes. It only describes the operation and its arguments, leaving the agent to infer 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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