Skip to main content
Glama

set_title

Update title metadata for selected photos in Lightroom Classic to organize and identify images efficiently.

Instructions

Set Lightroom title metadata for selected photos or local_ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
local_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'set_title' tool, which sets Lightroom title metadata.
    @mcp.tool()
    async def set_title(title: str, local_ids: list[int] | None = None) -> dict[str, Any]:
        """Set Lightroom title metadata for selected photos or local_ids."""
        payload: dict[str, Any] = {"title": str(title)}
        ids = validate_local_ids(local_ids)
        if ids:
            payload["local_ids"] = ids
        return await _call("metadata.set_title", 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 but lacks behavioral details. It implies a mutation ('Set'), but doesn't disclose permissions needed, whether changes are reversible, rate limits, or what happens if 'local_ids' is null. This leaves significant gaps for an agent to understand the tool's behavior.

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, clearly front-loading the core action. It's appropriately sized for the tool's complexity.

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 2 parameters with 0% schema coverage and no annotations, but with an output schema present, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, parameter usage, and context, leaving the agent to rely heavily on the output schema and trial-and-error.

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 details. The description adds minimal semantics by mentioning 'title' and 'local_ids', but doesn't explain their roles, formats, or interactions (e.g., that 'local_ids' can be null to use selected photos). This partially compensates but is insufficient for full understanding.

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 the resource ('Lightroom title metadata for selected photos or local_ids'), making the purpose understandable. It doesn't explicitly differentiate from siblings like 'set_caption' or 'set_label', but the focus on title metadata is specific enough for basic understanding.

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 'set_caption' or 'set_label', nor are there any prerequisites or context for usage mentioned. The description only states what it does without indicating appropriate scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/4xiomdev/lightroom-classic-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server