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Australian Bureau of Statistics (ABS)

by seansoreilly
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TypeScript
Remote

ABS MCP Server

An MCP (Model Context Protocol) server that provides access to the Australian Bureau of Statistics (ABS) Data API. This server allows AI assistants to query and analyze ABS statistical data through the SDMX-ML API.

Features

  • Dynamic discovery of all available ABS datasets via SDMX-ML API

  • Query ABS datasets with optional filters

  • Support for multiple data formats (JSON, CSV, XML)

  • Built on the MCP protocol for seamless integration with AI assistants

  • Caching system for improved performance

  • Comprehensive logging and error handling

Related MCP server: Australian Bureau of Statistics

Installation

npm install

Development

Prerequisites

  • Node.js 18 or higher

  • npm 8 or higher

Building

npm run build

Running

npm start

Development Tools

  • npm run build: Build the TypeScript code

  • npm start: Run the server

  • npm run inspector: Run the MCP inspector for testing

Project Structure

src/
├── index.ts                # Main server implementation
├── services/
│   └── abs/
│       ├── ABSApiClient.ts # ABS API communication
│       └── DataFlowService.ts # Data flow management and caching
├── types/
│   └── abs.ts             # TypeScript type definitions
└── utils/
    └── logger.ts          # Logging configuration

Implementation Details

ABS API Client

The ABSApiClient class handles communication with the ABS Data API:

  • Uses SDMX-ML format for data exchange

  • Supports multiple response formats (JSON, CSV, XML)

  • Implements proper error handling and logging

  • Configurable timeouts and retries

Data Flow Service

The DataFlowService class manages ABS data flows:

  • Dynamically fetches available datasets from ABS API

  • Implements caching with configurable refresh intervals

  • Provides methods for querying specific datasets

  • Handles data transformation and formatting

Logging

Comprehensive logging system using Winston:

  • Debug-level logging for development

  • Structured JSON logging format

  • Console and file transport options

  • Configurable log levels and formats

Integration with Claude Desktop

  1. Close Claude Desktop if it's running

  2. Start the ABS MCP server: npm start

  3. Start Claude Desktop

  4. The ABS tools should appear in the "Available MCP Tools" window

API Documentation

For more information about the ABS Data API:

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Submit a pull request

License

MIT License

Available Tools

1 tool
query_datasetC

Query a specific ABS dataset with optional filters

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

TDQS

C2.6/5.0
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 'query' but doesn't clarify if this is a read-only operation, what permissions are needed, potential rate limits, or the response format. The mention of 'optional filters' adds some context, but critical behavioral traits like safety, performance, or output characteristics are missing, leaving significant gaps.

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 a single, clear sentence that efficiently conveys the core action and key feature (optional filters). It's front-loaded with the main purpose and avoids unnecessary words. However, it could be slightly more informative without losing conciseness, such as hinting at the query's output or data scope.

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 tool's complexity (a query operation with potential filters), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the query returns (e.g., raw data, aggregated results), how filters are applied, or any constraints. For a tool that likely involves data retrieval and processing, more context is needed to ensure proper usage.

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 description adds minimal value beyond the input schema, which has 100% coverage for the single parameter 'datasetId'. It implies filtering is possible but doesn't detail how filters work or their syntax. With high schema coverage, the baseline is 3, as the schema already documents the parameter adequately, and the description doesn't significantly enhance understanding of parameter usage or semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('query') and target ('ABS dataset'), which provides a basic understanding of purpose. However, it's vague about what 'query' entails (e.g., retrieving data, statistics, or metadata) and doesn't specify resource scope beyond 'dataset'. With no siblings, differentiation isn't needed, but the purpose lacks specificity beyond general terms.

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' as a usage hint, implying it can be used with or without filtering. However, it provides no guidance on when to use this tool (e.g., for data analysis vs. other operations) or any prerequisites. With no sibling tools, alternatives aren't relevant, but the lack of contextual guidance results in minimal assistance for decision-making.

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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TDQS

C2.9/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as querying ABS datasets with filters, making it distinct by default.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'query_dataset' follows a clear verb_noun pattern, which would be consistent if more tools were added.

Tool Count2/5

A single tool is too few for a server representing a national statistics bureau, which likely has multiple datasets, metadata operations, or data retrieval methods. This minimal scope feels thin and inadequate for the apparent domain.

Completeness2/5

The tool surface is severely incomplete for accessing ABS data. There are obvious gaps such as listing available datasets, retrieving metadata, or supporting other common statistical operations, which will limit agent functionality.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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