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set_caption

Add descriptive captions to selected photos in Lightroom Classic to organize and document your image collection.

Instructions

Set Lightroom caption metadata for selected photos or local_ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
captionYes
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `set_caption` tool handler is decorated with `@mcp.tool()` and executes a call to the Lightroom metadata API via `_call("metadata.set_caption", payload)`.
    @mcp.tool()
    async def set_caption(caption: str, local_ids: list[int] | None = None) -> dict[str, Any]:
        """Set Lightroom caption metadata for selected photos or local_ids."""
        payload: dict[str, Any] = {"caption": str(caption)}
        ids = validate_local_ids(local_ids)
        if ids:
            payload["local_ids"] = ids
        return await _call("metadata.set_caption", 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 is a 'Set' operation (implying mutation) but doesn't mention permission requirements, whether changes are reversible, rate limits, or what happens if local_ids are null. The description adds minimal behavioral context beyond the basic action.

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 resource, making it immediately understandable despite its brevity.

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 2 parameters, 0% schema coverage, no annotations, but with an output schema, the description is minimally adequate. It states what the tool does but lacks behavioral details, parameter explanations, and usage guidance. The output schema reduces the need to describe return values, but significant gaps remain.

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 'caption' and 'local_ids' parameters by context but doesn't explain their semantics, formats, or what happens when local_ids is null. It adds marginal value by naming the parameters but lacks detailed guidance.

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'), the target resource ('Lightroom caption metadata'), and the scope ('for selected photos or local_ids'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'set_title' or 'set_label' that also modify metadata fields.

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 like 'set_title' or 'set_label', nor does it mention prerequisites (e.g., needing photos selected in Lightroom) or exclusions. Usage context is implied but not explicitly stated.

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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