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apply_develop_settings

Apply develop settings like exposure, contrast, and split toning to selected photos in Lightroom Classic. Configure strict parameter validation, auto-clamp values, and create named undo steps for editing workflows.

Instructions

Apply one or many develop settings to selected photos or local_ids.

Settings is a dict of parameter names to values, e.g.: {"Exposure": 0.5, "Contrast": 25, "SplitToningHighlightHue": 35} Use strict=True to reject unknown parameters. clamp=True (default) auto-clamps to valid ranges. history_name creates a named undo step in Lightroom.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
settingsYes
local_idsNo
strictNo
clampNo
history_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for 'apply_develop_settings'. It invokes '_apply_validated_settings' to process the parameters.
    async def apply_develop_settings(
        settings: dict[str, Any],
        local_ids: list[int] | None = None,
        strict: bool = False,
        clamp: bool = True,
        history_name: str | None = None,
    ) -> dict[str, Any]:
        """Apply one or many develop settings to selected photos or local_ids.
    
        Settings is a dict of parameter names to values, e.g.:
        {"Exposure": 0.5, "Contrast": 25, "SplitToningHighlightHue": 35}
        Use strict=True to reject unknown parameters. clamp=True (default) auto-clamps to valid ranges.
        history_name creates a named undo step in Lightroom.
        """
        response = await _apply_validated_settings(
            settings,
            local_ids=local_ids,
            strict=strict,
            clamp=clamp,
            history_name=history_name,
        )
        return response
  • The internal helper '_apply_validated_settings' which performs the actual validation and communication with the underlying Lightroom system.
    async def _apply_validated_settings(
        settings: dict[str, Any],
        *,
        local_ids: list[int] | None = None,
        strict: bool = False,
        clamp: bool = True,
        history_name: str | None = None,
    ) -> dict[str, Any]:
        ids = validate_local_ids(local_ids)
        result = validate_develop_settings(settings, strict=strict, clamp=clamp)
    
        payload: dict[str, Any] = {"settings": result.sanitized}
        if ids:
            payload["local_ids"] = ids
        if history_name:
            payload["history_name"] = history_name
    
        response = await _call("develop.apply_settings", payload)
        response["validation_warnings"] = result.warnings
        response["applied_settings"] = result.sanitized
        return response
  • Registration of the 'apply_develop_settings' tool using the @mcp.tool() decorator.
    @mcp.tool()
    async def apply_develop_settings(
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses some behavioral traits: it mentions 'strict=True to reject unknown parameters' and 'clamp=True (default) auto-clamps to valid ranges', which are important for understanding how the tool handles input. However, it doesn't cover permission requirements, side effects on photos, or error handling.

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 efficiently structured: first sentence states the purpose, followed by a clear example for the main parameter, then concise explanations of key boolean flags. Every sentence adds value with zero wasted words, making it easy to parse.

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 5 parameters with 0% schema coverage and no annotations, the description does a good job explaining the core functionality and key parameters. The presence of an output schema reduces the need to describe return values. However, it doesn't fully address all parameters (like 'local_ids') or provide comprehensive behavioral context for a mutation tool.

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?

Schema description coverage is 0%, so the description must compensate. It effectively explains the 'settings' parameter with a concrete example dict, clarifies 'strict' and 'clamp' behaviors, and mentions 'history_name' creates an undo step. This adds significant meaning beyond the bare schema, though it doesn't cover 'local_ids' parameter semantics.

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 the action ('Apply') and target ('develop settings to selected photos or local_ids'), making the purpose understandable. It doesn't explicitly differentiate from siblings like 'apply_develop_preset' or 'set_develop_param', but the focus on custom settings dicts provides some implicit distinction.

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?

No explicit guidance on when to use this tool versus alternatives like 'apply_develop_preset' or 'set_develop_param'. The description mentions what the tool does but doesn't provide context about appropriate use cases or prerequisites beyond the basic functionality.

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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