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get_reference_frames

Extract scene frames from reference videos for AI analysis, enabling automated video structure identification and project variation generation in the Statonic editor.

Instructions

Read the pending reference video analysis request and return each scene frame as an image for you to analyze.

After the user clicks "Copy Reference" in the editor and selects a video, call this tool to see the extracted frames. Then call write_reference_result with your analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that this is a read operation ('Read... and return') and describes the workflow context, but doesn't mention potential behavioral aspects like error conditions, rate limits, authentication needs, or what happens if no pending request exists. It adds some context but leaves gaps.

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 perfectly concise with two sentences that each earn their place: the first explains what the tool does, the second explains when to use it and what to do next. There's zero wasted text and it's front-loaded with the core functionality.

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 zero parameters, no annotations, and no output schema, the description provides good context about the workflow and sequencing. However, it doesn't describe the return format (frames as images) in detail or mention potential edge cases. For a read-only tool with this complexity level, it's mostly complete but could benefit from slightly more behavioral detail.

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?

The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose and usage. A baseline of 4 is appropriate for zero-parameter tools.

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

Purpose5/5

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

The description clearly states the specific action ('Read the pending reference video analysis request and return each scene frame as an image for you to analyze') and distinguishes it from siblings like 'get_frames' by specifying it's for reference video analysis requests triggered by user actions. It provides a complete verb+resource+scope combination.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('After the user clicks "Copy Reference" in the editor and selects a video, call this tool') and what to do next ('Then call write_reference_result with your analysis'). It provides clear sequencing and alternative guidance by naming the follow-up tool.

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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