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reset_current_photo

Reset all develop adjustments on the currently active photo in Lightroom Classic. Use this tool to revert edits and restore the original image state.

Instructions

Reset all develop adjustments on the currently active photo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Tool handler for resetting the active photo's develop adjustments, which calls the 'develop.reset_current_photo' command on the bridge.
    async def reset_current_photo() -> dict[str, Any]:
        """Reset all develop adjustments on the currently active photo."""
        return await _call("develop.reset_current_photo")
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Reset') but does not clarify whether this is destructive (e.g., irreversible without undo), requires specific permissions, or has side effects. The phrase 'all develop adjustments' implies a broad scope, but behavioral traits like confirmation prompts or error conditions are omitted.

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 a single, efficient sentence that front-loads the core action ('Reset all develop adjustments') and target ('currently active photo'). There is no wasted verbiage, repetition, or unnecessary elaboration, making it highly concise and well-structured for quick comprehension.

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?

Given the tool's zero parameters and the presence of an output schema (which handles return values), the description is minimally adequate. However, as a mutation tool with no annotations, it lacks details on behavioral aspects like safety, reversibility, or error handling, which are important for contextual understanding despite the simple schema.

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 zero parameters, and the input schema has 100% description coverage (though empty). The description adds no parameter information, which is appropriate since none exist. A baseline score of 4 is assigned as the description does not need to compensate for any parameter documentation gaps.

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 ('Reset all develop adjustments') and the target resource ('on the currently active photo'), using precise terminology that distinguishes it from sibling tools like 'apply_develop_settings' or 'undo' which perform different operations. It directly communicates the tool's function without redundancy.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., requiring an active photo), exclusions, or comparisons to related tools like 'undo', 'apply_snapshot', or 'apply_develop_preset', leaving the agent to infer usage context from the tool name alone.

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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