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demo_vid_refine

Adjust video timing or narration by providing natural-language feedback on a specific video. If script changes are needed, it directs you to regenerate the video with an updated script.

Instructions

Re-generate a video with timing or narration adjustments.

[RATIONALE] This tool is a stub — refinement requires LLM-driven script mutation which is not yet implemented. It returns a helpful message pointing to the working alternative (demo_vid_generate with an updated script).

Return Format

{"success": False, "error": str, "suggestions": list}

Examples

await demo_vid_refine(video_name="chitchat-final", feedback="Make step 2 narration longer")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedbackYesNatural-language description of changes needed.
video_nameYesName of the video to refine (e.g. chitchat-final).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description honestly discloses that the tool is a stub and returns a helpful message, with a stated return format. Annotations are absent, but the description provides clear behavioral context, including the 'success': False indication and suggestions list.

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 description is well-structured with clear sections (rationale, return format, examples) and is appropriately sized for a stub tool. No wasted sentences, though the rationale could be more concise.

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 is a stub, the description adequately covers its limited functionality and return format. The presence of an output schema and example adds completeness. No significant gaps for this context.

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?

The input schema has 100% description coverage, providing clear meanings for 'feedback' and 'video_name'. The description adds little extra beyond the schema, but an example use case is provided, which aids understanding. Baseline 3 is appropriate.

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 clearly states that the tool re-generates a video with timing or narration adjustments, distinguishing it from sibling tools like demo_vid_generate. However, it mentions it is a stub, which may slightly reduce clarity for agents expecting full functionality.

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

Usage Guidelines3/5

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

The description explicitly points to the working alternative (demo_vid_generate with updated script), offering good when-to-use guidance. However, it doesn't elaborate on when to use this tool vs other siblings, leaving some context implicit.

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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