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clanker25
by clanker25

record_trajectory_feedback

Record accept, reject, undo, or correction feedback for a captured Blender trajectory step, with optional step index and correction text.

Instructions

Record evaluation feedback for a captured trajectory step.

Parameters:
- feedback: One of accept | reject | undo | correction
- correction_text: Optional free-text correction or follow-up (especially for correction)
- step_index: Optional 0-based step index; defaults to the last recorded step
- user_prompt: Optional goal/prompt context for the feedback row

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedbackYes
step_indexNo
user_promptNo
correction_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context such as the default step_index and the relationship between correction_text and correction feedback. However, it does not mention whether feedback is appended or overwritten, whether a prior captured step is required, or any side effects beyond recording.

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 compact and purpose-driven. It opens with a one-sentence definition, then uses a clean parameter list that adds value without repeating the schema verbatim.

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

Completeness4/5

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

Given the tool's low complexity and the presence of an output schema, the description is nearly complete: all parameters are covered and defaults are stated. Minor gaps are minor, such as whether feedback can be re-recorded or edited for the same step, and the implicit prerequisite of an existing captured trajectory step.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by explaining every parameter: the allowed feedback values, the optional correction_text, the 0-based step_index with its default, and the user_prompt context. This is exactly the kind of semantic detail the schema lacks.

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?

The description uses a clear verb-object structure: 'Record evaluation feedback for a captured trajectory step.' This clearly identifies the resource and action, and the trajectory-specific language sets it apart from the surrounding Blender/modeling tools. It does not explicitly differentiate from a sibling, but no sibling is close enough to require that.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives practical usage context: feedback can be one of four values, correction_text is especially relevant for corrections, and step_index defaults to the last recorded step. It does not explicitly state when not to use the tool or name alternatives, but the context is clear and no alternative tool is apparent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.