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auto_tone

Apply automatic tone adjustments to selected photos in Lightroom Classic to optimize exposure, contrast, and color balance.

Instructions

Run Auto Tone style adjustments for selected photos or local_ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'auto_tone' function is defined as an asynchronous handler decorated with @mcp.tool(). It validates local_ids and calls the underlying Lightroom 'develop.auto_tone' method.
    async def auto_tone(local_ids: list[int] | None = None) -> dict[str, Any]:
        """Run Auto Tone style adjustments for selected photos or local_ids."""
        ids = validate_local_ids(local_ids)
        payload = {"local_ids": ids} if ids else {}
        return await _call("develop.auto_tone", payload)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this 'adjusts' photos, implying a mutation, but doesn't specify whether changes are destructive, reversible (via undo), require specific permissions, or have side effects. It also doesn't mention what 'Auto Tone' entails algorithmically or how it interacts with other adjustments.

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 with zero wasted words. It's front-loaded with the core action and immediately specifies the target, making it easy to parse quickly without unnecessary elaboration.

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 that there's an output schema (which handles return values), no annotations, and low schema coverage, the description is minimally adequate. It covers the basic purpose but lacks behavioral details, usage context, and fuller parameter semantics that would help an agent use this mutation tool correctly in a complex sibling tool environment.

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?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions 'local_ids' as an option alongside 'selected photos', adding some context about what the parameter represents. However, it doesn't explain the relationship between these two input modes or provide examples, leaving gaps in understanding how to use the parameter effectively.

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 ('Run Auto Tone style adjustments') and target ('for selected photos or local_ids'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from similar sibling tools like 'auto_white_balance' or 'apply_develop_settings', which might also perform automated adjustments.

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 mentions 'selected photos or local_ids' but doesn't explain prerequisites (e.g., whether photos must be in develop mode) or compare it to other auto-adjustment tools like 'auto_white_balance' or manual adjustment tools.

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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