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boshyxd

Roblox Studio MCP Server

mass_get_property

Retrieve a specific property from multiple instances simultaneously using a structured query, simplifying data extraction in Roblox Studio environments.

Instructions

Get the same property from multiple instances at once

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathsYesArray of instance paths to read from
propertyNameYesName of the property to get

Implementation Reference

  • The core MCP tool handler for mass_get_property. Validates input parameters (paths array and propertyName), forwards the request to the Studio bridge via HTTP client to /api/mass-get-property, and returns an MCP-formatted response containing the JSON-stringified result.
    async massGetProperty(paths: string[], propertyName: string) {
      if (!paths || paths.length === 0 || !propertyName) {
        throw new Error('Paths array and property name are required for mass_get_property');
      }
      const response = await this.client.request('/api/mass-get-property', { 
        paths, 
        propertyName
      });
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(response, null, 2)
          }
        ]
      };
    }
  • The input schema definition for the mass_get_property tool, provided in response to ListToolsRequest. Specifies required parameters: paths (string array) and propertyName (string).
      name: 'mass_get_property',
      description: 'Get the same property from multiple instances at once',
      inputSchema: {
        type: 'object',
        properties: {
          paths: {
            type: 'array',
            items: { type: 'string' },
            description: 'Array of instance paths to read from'
          },
          propertyName: {
            type: 'string',
            description: 'Name of the property to get'
          }
        },
        required: ['paths', 'propertyName']
      }
    },
  • src/index.ts:682-683 (registration)
    Registration of the mass_get_property tool in the MCP CallToolRequest handler. Dispatches tool calls to the RobloxStudioTools.massGetProperty method with extracted arguments.
    case 'mass_get_property':
      return await this.tools.massGetProperty((args as any)?.paths as string[], (args as any)?.propertyName as string);
  • HTTP proxy endpoint in the bridge server that exposes mass_get_property for direct calls from the Studio plugin, forwarding to the tools handler.
    app.post('/mcp/mass_get_property', async (req, res) => {
      try {
        const result = await tools.massGetProperty(req.body.paths, req.body.propertyName);
        res.json(result);
      } catch (error) {
        res.status(500).json({ error: error instanceof Error ? error.message : 'Unknown error' });
      }
    });
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 only states the basic operation without disclosing behavioral traits. It doesn't mention whether this is read-only (implied by 'Get' but not explicit), rate limits, error handling for invalid paths, or output format, which is critical for a tool handling multiple instances.

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 purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly.

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 batch operations and lack of annotations or output schema, the description is incomplete. It doesn't explain return values, error scenarios, or performance implications, leaving significant gaps for an AI agent to understand how to use this tool effectively in context.

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 100%, so the schema fully documents parameters (paths and propertyName). The description adds no additional meaning beyond implying batch processing, which doesn't enhance parameter understanding beyond what the schema provides. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('Get') and target ('the same property from multiple instances at once'), distinguishing it from single-instance tools like get_instance_properties. However, it doesn't explicitly differentiate from other mass operations like mass_create_objects or mass_set_property, which prevents a perfect score.

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 like get_instance_properties (for single instances) or search_by_property (for property-based queries). It lacks context on prerequisites, performance trade-offs, or error handling, leaving usage decisions ambiguous.

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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