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Australian Bureau of Statistics (ABS)

by seansoreilly

ABS MCP 서버

호주 통계청(ABS) 데이터 API에 대한 액세스를 제공하는 MCP(모델 컨텍스트 프로토콜) 서버입니다. 이 서버를 통해 AI 지원자는 SDMX-ML API를 통해 ABS 통계 데이터를 쿼리하고 분석할 수 있습니다.

특징

  • SDMX-ML API를 통해 사용 가능한 모든 ABS 데이터세트의 동적 검색

  • 선택적 필터를 사용하여 ABS 데이터 세트 쿼리

  • 다양한 데이터 형식(JSON, CSV, XML) 지원

  • AI 어시스턴트와의 원활한 통합을 위해 MCP 프로토콜 기반으로 구축

  • 성능 향상을 위한 캐싱 시스템

  • 포괄적인 로깅 및 오류 처리

Related MCP server: Australian Bureau of Statistics

설치

지엑스피1

개발

필수 조건

  • Node.js 18 이상

  • npm 8 이상

건물

npm run build

달리기

npm start

개발 도구

  • npm run build : TypeScript 코드 빌드

  • npm start : 서버 실행

  • npm run inspector : 테스트를 위해 MCP 검사기를 실행합니다.

프로젝트 구조

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

구현 세부 사항

ABS API 클라이언트

ABSApiClient 클래스는 ABS 데이터 API와의 통신을 처리합니다.

  • 데이터 교환을 위해 SDMX-ML 형식을 사용합니다.

  • 다양한 응답 형식(JSON, CSV, XML)을 지원합니다.

  • 적절한 오류 처리 및 로깅을 구현합니다.

  • 구성 가능한 시간 초과 및 재시도

데이터 흐름 서비스

DataFlowService 클래스는 ABS 데이터 흐름을 관리합니다.

  • ABS API에서 사용 가능한 데이터 세트를 동적으로 가져옵니다.

  • 구성 가능한 새로 고침 간격으로 캐싱을 구현합니다.

  • 특정 데이터세트를 쿼리하기 위한 방법을 제공합니다.

  • 데이터 변환 및 포맷을 처리합니다.

벌채 반출

Winston을 사용한 포괄적인 로깅 시스템:

  • 개발을 위한 디버그 수준 로깅

  • 구조화된 JSON 로깅 형식

  • 콘솔 및 파일 전송 옵션

  • 구성 가능한 로그 수준 및 형식

Claude Desktop과 통합

  1. Claude Desktop이 실행 중이면 닫으세요.

  2. ABS MCP 서버를 시작합니다: npm start

  3. Claude Desktop 시작

  4. ABS 도구는 "사용 가능한 MCP 도구" 창에 나타나야 합니다.

API 문서

ABS 데이터 API에 대한 자세한 내용은 다음을 참조하세요.

기여하다

  1. 저장소를 포크하세요

  2. 기능 브랜치 생성

  3. 변경 사항을 만드세요

  4. 풀 리퀘스트 제출

특허

MIT 라이센스

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedquery_dataset

TDQS

C2.9/5.0

Scored across 1 tool

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

ActivityMaintained
ResponsivenessUnresponsive

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