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bulc_list_result_datasets

Read-only

Lists available datasets from FDS fire simulation results, including slices, smoke3d volumes, Plot3D data, and devices, to help users analyze and visualize building design performance.

Instructions

List available datasets in the loaded FDS results. Returns slices, smoke3d volumes, Plot3D, devices, and other data types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoFilter by data type: 'slice', 'smoke3d', 'plot3d', 'device', 'all'. Default: all

Implementation Reference

  • Handler logic for the 'bulc_list_result_datasets' tool: validates input arguments using Zod schema and sends 'list_result_datasets' action to the BULC client.
    case "bulc_list_result_datasets": {
      const validated = ListResultDatasetsSchema.parse(args);
      result = await client.sendCommand({
        action: "list_result_datasets",
        params: validated,
      });
      break;
    }
  • Zod schema used for runtime input validation of the 'bulc_list_result_datasets' tool parameters.
    const ListResultDatasetsSchema = z.object({
      type: z.enum(["slice", "smoke3d", "plot3d", "device", "all"]).optional(),
    });
  • Tool registration object defining name, description, input schema, and annotations for 'bulc_list_result_datasets' in the resultTools export.
    {
      name: "bulc_list_result_datasets",
      description:
        "List available datasets in the loaded FDS results. " +
        "Returns slices, smoke3d volumes, Plot3D, devices, and other data types.",
      inputSchema: {
        type: "object" as const,
        properties: {
          type: {
            type: "string",
            description: "Filter by data type: 'slice', 'smoke3d', 'plot3d', 'device', 'all'. Default: all",
            enum: ["slice", "smoke3d", "plot3d", "device", "all"],
          },
        },
      },
      annotations: {
        readOnlyHint: true,
        destructiveHint: false,
      },
    },
  • src/index.ts:124-132 (registration)
    Top-level tool dispatch in MCP server: routes 'bulc_list_result_datasets' calls to the specific handleResultTool handler.
    if (
      name === "bulc_open_result_viewer" ||
      name === "bulc_list_result_datasets" ||
      name === "bulc_get_point_data" ||
      name === "bulc_run_aset_analysis" ||
      name === "bulc_generate_report"
    ) {
      return await handleResultTool(name, safeArgs);
    }
Behavior3/5

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

Annotations indicate readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds value by specifying the data types returned (slices, smoke3d volumes, etc.), which isn't covered by annotations. However, it doesn't disclose other behavioral traits like potential rate limits, error conditions, or whether it requires specific permissions beyond having loaded results. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, stating the main purpose in the first clause. The second clause adds useful detail about return types without redundancy. It's efficient with two sentences, though it could be slightly more structured by explicitly mentioning the optional filtering parameter upfront.

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's low complexity (1 optional parameter, no output schema), the description is somewhat complete but has gaps. It covers the purpose and return types, but lacks usage guidelines and detailed behavioral context. With annotations covering safety, it's adequate for a simple read operation, but could benefit from more context about when and how to use it effectively.

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, with a clear enum for the 'type' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, such as default behavior details or examples. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to heavily supplement the schema.

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: 'List available datasets in the loaded FDS results.' It specifies the verb ('List') and resource ('datasets in the loaded FDS results'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'bulc_get_fds_data' or 'bulc_get_point_data', which might also retrieve data, so it doesn't reach 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. It mentions the types of data returned (slices, smoke3d volumes, etc.), but doesn't specify prerequisites (e.g., requires loaded FDS results) or compare it to other data-fetching tools in the sibling list, such as 'bulc_get_fds_data' or 'bulc_get_point_data'. This leaves the agent without clear usage context.

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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