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apply_local_adjustment_settings

Adjust specific areas of photos in Lightroom Classic by modifying exposure, contrast, and clarity settings on active masks for targeted editing.

Instructions

Set local_* adjustment parameters on the currently active mask.

Settings dict uses local_* parameter names, e.g.: {"local_Exposure": 1.5, "local_Contrast": 25, "local_Clarity": 40} A mask must be active (selected) first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
settingsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The implementation of the apply_local_adjustment_settings tool which acts as a wrapper around the masks.set_local_settings call.
    @mcp.tool()
    async def apply_local_adjustment_settings(settings: dict[str, Any]) -> dict[str, Any]:
        """Set local_* adjustment parameters on the currently active mask.
    
        Settings dict uses local_* parameter names, e.g.:
        {"local_Exposure": 1.5, "local_Contrast": 25, "local_Clarity": 40}
        A mask must be active (selected) first.
        """
        if not settings:
            raise ValueError("settings dict is required")
        return await _call("masks.set_local_settings", {"settings": settings})
Behavior2/5

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

With no annotations provided, the description carries full burden but only mentions the mask prerequisite. It doesn't disclose whether this is a destructive/write operation (implied by 'Set'), what permissions are needed, rate limits, or what happens if no mask is active. The behavioral context is minimal for a tool that modifies settings.

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?

Three sentences with zero waste: first states the purpose, second provides parameter format with example, third gives critical prerequisite. Each sentence earns its place, and the structure is front-loaded with the core action.

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?

For a single-parameter mutation tool with no annotations but an output schema, the description covers the parameter well and states the prerequisite. However, it lacks behavioral details like error conditions or confirmation of changes. The output schema existence means return values don't need explanation, but more operational context would help.

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%, so the description must fully explain the parameter. It does this excellently: it names the parameter ('settings dict'), explains the format ('uses local_* parameter names'), provides a concrete example with specific keys and values, and clarifies it's an object with additionalProperties. This adds complete meaning beyond the bare schema.

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 ('Set local_* adjustment parameters'), target resource ('on the currently active mask'), and distinguishes it from siblings like 'set_develop_param' or 'apply_develop_settings' by focusing on local mask adjustments. It provides a concrete example of the settings format.

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 explicitly states the prerequisite 'A mask must be active (selected) first', which is crucial usage guidance. However, it doesn't mention when to use this tool versus alternatives like 'set_develop_param' for global adjustments or 'get_local_adjustment_settings' for reading values.

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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