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ABS MCP Server

by seansoreilly

query_dataset

Query Australian Bureau of Statistics datasets using SDMX-ML API to retrieve and analyze statistical data with optional filtering capabilities.

Instructions

Query a specific ABS dataset with optional filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetIdYesID of the dataset to query (e.g., C21_G01_LGA)

Implementation Reference

  • The handler function for CallToolRequestSchema that implements the logic for the 'query_dataset' tool, including validation, API call to ABS, and response formatting.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      try {
        const { name, arguments: args } = request.params;
    
        if (name !== "query_dataset") {
          throw new Error(`Unknown tool: ${name}`);
        }
    
        if (!args?.datasetId || typeof args.datasetId !== "string") {
          throw new Error("datasetId is required and must be a string");
        }
    
        const url = `${ABS_API_BASE}/data/${args.datasetId}/all?format=json&dimensionAtObservation=AllDimensions`;
        
        try {
          const response = await axios.get(url);
          return {
            content: [{
              type: "text",
              text: JSON.stringify(response.data, null, 2)
            }]
          };
        } catch (error) {
          if (error instanceof AxiosError && error.response) {
            throw new Error(`ABS API Error: ${error.response.status} - ${JSON.stringify(error.response.data)}`);
          }
          throw error;
        }
      } catch (error) {
        const errorMessage = error instanceof Error ? error.message : String(error);
        throw new Error(`Error querying dataset: ${errorMessage}`);
      }
    });
  • src/index.ts:29-48 (registration)
    Registers the 'query_dataset' tool in the ListToolsRequestSchema handler by including it in the tools list.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: [
          {
            name: "query_dataset",
            description: "Query a specific ABS dataset with optional filters",
            inputSchema: {
              type: "object",
              required: ["datasetId"],
              properties: {
                datasetId: {
                  type: "string",
                  description: "ID of the dataset to query (e.g., C21_G01_LGA)"
                }
              }
            }
          }
        ]
      };
    });
  • Input schema definition for the 'query_dataset' tool, specifying the required 'datasetId' parameter.
    inputSchema: {
      type: "object",
      required: ["datasetId"],
      properties: {
        datasetId: {
          type: "string",
          description: "ID of the dataset to query (e.g., C21_G01_LGA)"
        }
      }
    }
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool queries with optional filters but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what the output format is. This leaves significant gaps for a query tool.

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 with zero waste, clearly front-loading the core action. It's appropriately sized for a simple tool with one parameter.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the query returns, error conditions, or behavioral constraints. For a query tool, this leaves the agent without key operational 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 already documents the single parameter 'datasetId' with an example. The description adds no additional meaning beyond implying filtering options, which aren't detailed. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 ('query') and resource ('specific ABS dataset'), making the purpose understandable. However, it doesn't differentiate from siblings (none exist) and could be more specific about what 'query' entails beyond filtering.

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 mentions 'optional filters' but provides no guidance on when to use this tool versus alternatives, prerequisites, or typical use cases. With no siblings, differentiation isn't needed, but general usage context is missing.

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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