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apply_snapshot

Apply saved develop settings to photos in Lightroom Classic using snapshot IDs to restore specific editing states or replicate adjustments across images.

Instructions

Apply a develop snapshot by its ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
snapshot_idYes
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The apply_snapshot tool handler, which validates inputs and calls the develop.apply_snapshot method.
    @mcp.tool()
    async def apply_snapshot(
        snapshot_id: str,
        local_ids: list[int] | None = None,
    ) -> dict[str, Any]:
        """Apply a develop snapshot by its ID."""
        if not snapshot_id:
            raise ValueError("snapshot_id is required")
        ids = validate_local_ids(local_ids)
        payload: dict[str, Any] = {"snapshot_id": snapshot_id}
        if ids:
            payload["local_ids"] = ids
        return await _call("develop.apply_snapshot", payload)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'apply' which implies a mutation, but doesn't specify if this is destructive, reversible (e.g., via 'undo'), requires specific permissions, or affects the current photo state. More context on the tool's behavior is needed.

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 no wasted words, making it easy to parse. It's appropriately sized and front-loaded with the core action.

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 the tool has an output schema (which reduces the need to describe return values) but no annotations and low schema coverage, the description is incomplete. It covers the basic purpose but lacks usage guidelines, parameter details, and behavioral context needed for a mutation tool in a complex environment like Lightroom.

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 no parameter details. It mentions 'by its ID' which hints at 'snapshot_id', but doesn't explain what 'local_ids' does or how parameters interact. The description fails to provide meaningful semantics beyond the bare schema.

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 resource ('develop snapshot by its ID'), making the purpose understandable. However, it doesn't differentiate from similar sibling tools like 'apply_develop_preset' or 'apply_develop_settings', which also apply adjustments to photos in Lightroom.

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 like 'apply_develop_preset' or 'apply_develop_settings', nor does it mention prerequisites such as needing an existing snapshot from 'create_snapshot'. The description lacks context for appropriate usage.

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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