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Lightroom Classic MCP

Lightroom Classic MCP

by 4xiomdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_local_adjustment_settings

Retrieve local adjustment settings for the active mask in Lightroom Classic, including exposure, contrast, and highlights parameters.

Instructions

Read local_* adjustment parameters on the currently active mask.

Returns values for local_Exposure, local_Contrast, local_Highlights, etc. A mask must be active (selected) first.

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": "get_local_adjustment_settings"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/lightroom_mcp_custom/server.py:670-677 (handler)
    The implementation of the get_local_adjustment_settings tool, which calls masks.get_local_settings via an internal _call helper.
    @mcp.tool()
async def get_local_adjustment_settings() -> dict[str, Any]:
    """Read local_* adjustment parameters on the currently active mask.

    Returns values for local_Exposure, local_Contrast, local_Highlights, etc.
    A mask must be active (selected) first.
    """
    return await _call("masks.get_local_settings")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.7/5.0
Behavior4/5

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 successfully communicates this is a read operation ('Read', 'Returns values'), specifies the scope ('on the currently active mask'), and provides important behavioral context about the prerequisite condition. It doesn't mention error behavior, rate limits, or authentication needs, but provides sufficient core behavioral information.

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 perfectly concise with three sentences that each earn their place: the first states the core purpose, the second specifies the return values, and the third provides critical usage guidance. No wasted words, front-loaded with the most important information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a read operation with 0 parameters, 100% schema coverage, and an output schema exists, the description provides complete contextual information. It explains what the tool does, what it returns, and the prerequisite condition. The existence of an output schema means the description doesn't need to detail return format specifics.

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?

The tool has 0 parameters with 100% schema description coverage. The description doesn't need to explain parameters, but it does provide valuable context about what the tool returns ('Returns values for local_Exposure, local_Contrast, local_Highlights, etc.'), which adds semantic meaning beyond the empty input schema.

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 tool's purpose with specific verbs ('Read local_* adjustment parameters') and identifies the target resource ('on the currently active mask'). It distinguishes itself from siblings like 'get_develop_settings' or 'get_develop_group_settings' by focusing specifically on local adjustment parameters associated with masks.

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 provides explicit usage guidance: 'A mask must be active (selected) first.' This clearly states a prerequisite condition for successful tool invocation. It also implicitly distinguishes this tool from other get_* tools by specifying it operates on mask-specific parameters.

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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