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bbernstein

LacyLights MCP Server

by bbernstein

reorder_cues

Assign new cue numbers to multiple cues in a cue list on LacyLights MCP Server, enabling precise reordering for theatrical lighting design.

Instructions

Reorder multiple cues by assigning new cue numbers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cueListIdYesCue list ID containing the cues
cueReorderingYesArray of cue ID and new number pairs

Implementation Reference

  • The handler function for the 'reorder_cues' tool. It reorders cues in a specified cue list by updating the cueNumber of each cue using batched GraphQL mutations via the graphqlClient. Returns the list of updated cues with success status.
    async reorderCues(args: {
      cueListId: string;
      cueReordering: Array<{
        cueId: string;
        newCueNumber: number;
      }>;
    }) {
      const { cueListId, cueReordering } = args;
    
      try {
        // Update each cue with its new number
        const updatePromises = cueReordering.map(({ cueId, newCueNumber }) =>
          this.graphqlClient.updateCue(cueId, { cueNumber: newCueNumber }),
        );
    
        const updatedCues = await Promise.all(updatePromises);
    
        return {
          cueListId,
          updatedCues: updatedCues.map((cue: any) => ({
            cueId: cue.id,
            name: cue.name,
            cueNumber: cue.cueNumber,
          })),
          success: true,
          totalUpdated: updatedCues.length,
        };
      } catch (error) {
        throw new Error(`Failed to reorder cues: ${error}`);
      }
    }
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 action ('reorder multiple cues') but doesn't cover critical traits: whether this is a destructive mutation (likely yes, as it changes order), permission requirements, error handling (e.g., duplicate cue numbers), or response format. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its 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 that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('reorder multiple cues') and specifies the mechanism ('by assigning new cue numbers'), making it easy to parse. Every part of the sentence contributes essential information, earning its place.

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

Completeness2/5

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

Given the complexity of a mutation tool (reordering cues likely involves side effects), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, permissions, or what happens to other cues not in the reordering array. For a tool with 2 parameters and no structured support, more context is needed to guide effective use.

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?

The input schema has 100% description coverage, clearly documenting both parameters ('cueListId' and 'cueReordering') with their purposes. The description adds no additional semantic context beyond implying reordering involves 'assigning new cue numbers', which is already covered by the schema's details on 'newCueNumber'. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 verb ('reorder') and resource ('cues'), specifying it involves assigning new cue numbers. It distinguishes from siblings like 'remove_cue_from_list' or 'update_cue' by focusing on reordering multiple cues rather than deletion or single updates. However, it doesn't explicitly differentiate from 'optimize_cue_timing' or 'create_cue_sequence', which might involve ordering aspects, leaving some ambiguity.

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 (e.g., needing an existing cue list), exclusions, or comparisons to siblings like 'update_cue_list' (which might handle ordering) or 'optimize_cue_timing' (which could involve reordering for timing). Without such context, the agent must infer usage from the name and schema alone.

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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