Skip to main content
Glama

lightroom_ping

Verify Lightroom Classic connectivity and plugin health to ensure reliable communication between AI agents and the photo editing software.

Instructions

Verify Lightroom bridge connectivity and plugin health.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The lightroom_ping tool is implemented as an async function decorated with @mcp.tool(), which calls the underlying system.ping method.
    @mcp.tool()
    async def lightroom_ping() -> dict[str, Any]:
        """Verify Lightroom bridge connectivity and plugin health."""
        return await _call("system.ping")
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool verifies connectivity and health but doesn't describe what 'verify' entails (e.g., returns status codes, error messages, or detailed diagnostics), whether it's safe or has side effects, or any performance considerations like timeouts. This leaves significant gaps in understanding 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: 'Verify Lightroom bridge connectivity and plugin health.' It's front-loaded with the core purpose, has no redundant words, and every part earns its place by specifying what is being verified. This is an excellent example of conciseness.

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 0 parameters, 100% schema coverage, and an output schema exists, the description's job is simplified. It states the purpose clearly but lacks details on behavioral aspects like what the verification entails or output format. With no annotations, it should provide more context on safety or performance, but the output schema may cover return values, keeping it minimally adequate.

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, and schema description coverage is 100%. The description doesn't need to add parameter semantics, as there are none to explain. It appropriately focuses on the tool's purpose without unnecessary parameter details, meeting the baseline for zero-parameter tools.

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 tool's purpose: 'Verify Lightroom bridge connectivity and plugin health.' It specifies the action ('verify') and the target (Lightroom bridge connectivity and plugin health). However, it doesn't explicitly differentiate from sibling tools like 'lightroom_status' or 'lightroom_list_commands', which may have overlapping diagnostic functions.

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. It doesn't mention prerequisites, timing, or compare it to sibling tools like 'lightroom_status' or 'lightroom_list_commands'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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