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by using76

bulc_get_point_data

Read-only

Extract time-series simulation data for temperature, visibility, or species concentration at specific coordinates in BULC Building Designer.

Instructions

Extract time-series data at a specific point in the simulation domain. Useful for getting temperature, visibility, or species concentration at a location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYesX coordinate in meters
yYesY coordinate in meters
zYesZ coordinate in meters
variableYesVariable to extract: 'temp', 'visibility', 'co', 'co2', 'o2', 'velocity'
dataSourceNoData source: 'plot3d' or 'slice'. Default: plot3d

Implementation Reference

  • Handler logic for the 'bulc_get_point_data' MCP tool: parses input schema and sends 'get_point_data' action command to the BULC client.
    case "bulc_get_point_data": {
      const validated = GetPointDataSchema.parse(args);
      result = await client.sendCommand({
        action: "get_point_data",
        params: validated,
      });
      break;
  • Zod schema used for input validation in the 'bulc_get_point_data' tool handler.
    const GetPointDataSchema = z.object({
      x: z.number(),
      y: z.number(),
      z: z.number(),
      variable: z.enum(["temp", "visibility", "co", "co2", "o2", "velocity"]),
      dataSource: z.enum(["plot3d", "slice"]).optional(),
    });
  • MCP tool registration definition for 'bulc_get_point_data', including name, description, input schema, and annotations.
    {
      name: "bulc_get_point_data",
      description:
        "Extract time-series data at a specific point in the simulation domain. " +
        "Useful for getting temperature, visibility, or species concentration at a location.",
      inputSchema: {
        type: "object" as const,
        properties: {
          x: {
            type: "number",
            description: "X coordinate in meters",
          },
          y: {
            type: "number",
            description: "Y coordinate in meters",
          },
          z: {
            type: "number",
            description: "Z coordinate in meters",
          },
          variable: {
            type: "string",
            description: "Variable to extract: 'temp', 'visibility', 'co', 'co2', 'o2', 'velocity'",
            enum: ["temp", "visibility", "co", "co2", "o2", "velocity"],
          },
          dataSource: {
            type: "string",
            description: "Data source: 'plot3d' or 'slice'. Default: plot3d",
            enum: ["plot3d", "slice"],
          },
        },
        required: ["x", "y", "z", "variable"],
      },
      annotations: {
        readOnlyHint: true,
        destructiveHint: false,
      },
    },
  • src/index.ts:124-132 (registration)
    Main server request handler dispatching 'bulc_get_point_data' tool calls to the specific result tool 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 read-only and non-destructive behavior, which the description doesn't contradict. The description adds value by specifying the types of data retrievable (e.g., temperature, visibility) and the simulation domain context, but it doesn't disclose additional behavioral traits like rate limits, authentication needs, or output format details, which are relevant given the lack of an output schema.

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 highly concise and well-structured, consisting of two sentences that efficiently convey the core purpose and utility without any redundant information. It is front-loaded with the main action and avoids unnecessary elaboration.

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 complexity (point-specific data extraction in a simulation), annotations cover safety, and the schema fully describes inputs, but the lack of an output schema means the description should ideally hint at return values (e.g., time-series format). It partially compensates by listing data types, but more context on output behavior would enhance completeness for effective agent 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?

With 100% schema description coverage, the input schema fully documents all parameters. The description adds minimal semantics by listing example variables ('temperature, visibility, or species concentration') that align with the 'variable' enum, but it doesn't provide extra context beyond what the schema already specifies, such as coordinate system details or data source implications.

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 with a specific verb ('Extract') and resource ('time-series data at a specific point'), and identifies typical use cases ('temperature, visibility, or species concentration'). However, it doesn't explicitly differentiate from sibling tools like 'bulc_get_fds_data' or 'bulc_get_spatial_context', which might also retrieve data but with different scopes or formats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage context by stating it's 'useful for getting' specific variables at a location, which suggests when to use it. However, it lacks explicit guidance on when not to use it or alternatives among siblings, such as whether to prefer this over bulk data retrieval tools for point-specific queries.

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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