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set_rating

Assign star ratings (0-5) to photos in Lightroom Classic to organize and filter your catalog based on quality or preference.

Instructions

Set Lightroom star rating (0..5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ratingYes
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `set_rating` tool handler that validates the rating and calls the underlying bridge metadata command.
    async def set_rating(rating: int, local_ids: list[int] | None = None) -> dict[str, Any]:
        """Set Lightroom star rating (0..5)."""
        payload = {
            "rating": validate_rating(rating),
        }
        ids = validate_local_ids(local_ids)
        if ids:
            payload["local_ids"] = ids
        return await _call("metadata.set_rating", 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 but lacks behavioral details. It states the action ('Set') implying a mutation, but doesn't disclose permissions needed, whether changes are reversible (e.g., via 'undo'), rate limits, or what happens if 'local_ids' is null. It also doesn't describe the output format despite having an output schema.

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 waste. It's front-loaded with the core action and includes the critical rating range constraint. Every word earns its place, making it easy to parse quickly.

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 mutation tool with no annotations, 2 parameters, and an output schema, the description is minimally adequate. It covers the basic purpose and rating range, but lacks usage guidelines, behavioral context, and parameter details. The output schema reduces the need to describe return values, but more context is needed for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 but adds minimal parameter semantics. It mentions the 'rating' range (0..5) which isn't in the schema, but doesn't explain 'local_ids' (e.g., what they refer to, default behavior when null). With 2 parameters and low schema coverage, this is inadequate.

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 ('Set') and resource ('Lightroom star rating') with the specific range constraint (0..5). It distinguishes from siblings like 'set_label' or 'set_pick_status' by focusing on star ratings. However, it doesn't explicitly differentiate from all rating-related tools since there aren't any direct rating siblings.

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 guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing selected photos), exclusions, or comparisons to other tools like 'set_label' or 'set_pick_status' for metadata updates. The description is purely functional without contextual usage advice.

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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