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set_label

Assign or remove color label names to organize photos in Lightroom Classic for efficient catalog management and workflow automation.

Instructions

Set Lightroom color label name (or empty string to clear).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYes
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool 'set_label' is registered using the @mcp.tool() decorator and its handler implementation directly calls the metadata.set_label MCP backend method.
    @mcp.tool()
    async def set_label(label: str, local_ids: list[int] | None = None) -> dict[str, Any]:
        """Set Lightroom color label name (or empty string to clear)."""
        if label is None:
            raise ValueError("label must be a string")
        payload: dict[str, Any] = {"label": str(label)}
        ids = validate_local_ids(local_ids)
        if ids:
            payload["local_ids"] = ids
        return await _call("metadata.set_label", payload)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It implies a mutation ('Set'), but doesn't disclose behavioral traits like permissions needed, whether changes are reversible (e.g., via 'undo'), side effects, or rate limits. The mention of clearing with an empty string adds minimal context, but overall transparency is poor for a mutation tool.

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 essential detail (clearing option) without fluff. Every word earns its place, making it highly concise and well-structured.

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 a mutation tool with no annotations, 2 parameters (one undocumented), and an output schema (which reduces need to describe returns), the description is moderately complete. It covers the primary parameter's semantics but misses behavioral context and the second parameter, leaving gaps that could hinder effective tool use.

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 description must compensate. It explains the 'label' parameter's purpose and special case (empty string to clear), which adds meaningful semantics beyond the schema. However, it doesn't address the 'local_ids' parameter at all, leaving half the parameters undocumented. This partial compensation justifies a baseline score.

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 color label name'), and specifies the option to clear with an empty string. It distinguishes from siblings like 'set_rating' or 'set_title' by focusing on color labels. However, it doesn't explicitly contrast with other label-related tools (none exist in siblings), so it's not a perfect 5.

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 on when to use this tool versus alternatives is provided. The description mentions clearing with an empty string, but doesn't specify prerequisites (e.g., requires an active photo), exclusions, or compare to other setting tools like 'set_rating'. This leaves the agent without contextual usage cues.

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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