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Lightroom Classic MCP

Lightroom Classic MCP

by 4xiomdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

redo

Reverse the last undo action in Lightroom Classic to restore previous edits or adjustments.

Instructions

Redo the last undone Lightroom operation.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "redo"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/lightroom_mcp_custom/server.py:724-726 (handler)
    The redo function is defined as an MCP tool, which calls the system.redo Lightroom operation.
    async def redo() -> dict[str, Any]:
    """Redo the last undone Lightroom operation."""
    return await _call("system.redo")
  • src/lightroom_mcp_custom/server.py:723-724 (registration)
    The redo function is registered as an MCP tool using the @mcp.tool() decorator.
    @mcp.tool()
async def redo() -> dict[str, Any]:

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.1/5.0
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. While it states what the tool does, it doesn't disclose behavioral traits like whether it requires specific permissions, what happens if there's nothing to redo, or how it interacts with the Lightroom state. The description is minimal and lacks operational context.

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, front-loaded sentence with zero waste. Every word contributes directly to understanding the tool's purpose and usage context, making it maximally efficient for an AI agent.

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 no parameters, an output schema exists, and it's a simple operation, the description is reasonably complete for basic understanding. However, as a mutation tool with no annotations, it should ideally include more behavioral context about error conditions or state dependencies to be fully comprehensive.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0 parameters and 100% schema description coverage, the baseline is high. The description appropriately doesn't discuss parameters since none exist, and it correctly focuses on the tool's purpose rather than unnecessary parameter explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Redo') and the exact resource ('the last undone Lightroom operation'), distinguishing it from sibling tools like 'undo' and other editing commands. It uses precise terminology that matches the Lightroom context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly defines when to use this tool: after an 'undo' operation has been performed. It distinguishes from alternatives by specifying it applies to the 'last undone' action, not general redo operations or other editing 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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