Australian Bureau of Statistics (ABS)
Query Australian Bureau of Statistics (ABS) datasets through a single MCP tool.
query_datasetfetches an ABS dataset by its ID (e.g.C21_G01_LGA).Returns the SDMX-JSON payload as both text and structured content.
Includes a
resource_linkto the upstream ABS API URL.Requires no API key or credentials.
Communicates over stdio using the MCP protocol.
Handles SDMX-ML formatted data from the ABS Data API and allows returning results in XML format
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Australian Bureau of Statistics (ABS)show me the latest unemployment rate data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
The server currently exposes one tool:
query_dataset— fetches an ABS dataset by id (e.g.C21_G01_LGA) and returns the SDMX-JSON payload as both text andstructuredContent, with aresource_linkto the upstream URL. Declares atitle, read-only annotations, and anoutputSchema.
Built on MCP protocol revision 2025-11-25 (SDK v2, legacy era) over stdio.
Not yet exposed
ABSApiClient and DataFlowService implement dataset discovery, multi-format
support (JSON/CSV/XML), and on-disk caching with a configurable refresh interval.
They are fully tested but not currently wired into the server, so no tool
surfaces them yet — a list_dataflows tool is the natural next step. Treat the
section below as a description of the service layer, not of the tool surface.
Related MCP server: Australian Bureau of Statistics
Configuration
Variable | Default | Purpose |
|
| ABS API origin; override to point at a stub |
|
| Winston log level |
|
| Directory for log files |
|
| Where the dataflow cache is written |
|
| How long a cached dataflow list stays fresh |
|
| Request timeout for ABS API calls |
The ABS Data API is open — the server needs no API key or credentials.
All logging goes to stderr, never stdout — stdout carries the MCP JSON-RPC channel.
Installation
npm installDocker
A container image is published to GitHub Container Registry on every push to
main, tagged latest, main, sha-<commit>, and — for v* tags — the
semver version.
docker pull ghcr.io/seansoreilly/mcp-server-abs:latest
docker run -i --rm ghcr.io/seansoreilly/mcp-server-abs:latest-i is required: the server speaks MCP over stdio, so stdin and stdout are the
transport. There is no port to publish and no HTTP endpoint to health-check.
The image runs as the unprivileged node user and writes its logs and dataflow
cache to /data. Mount a volume there to keep the cache across restarts:
docker run -i --rm -v abs-mcp-data:/data ghcr.io/seansoreilly/mcp-server-abs:latestEvery variable in Configuration can be passed with -e; the
image presets ABS_CACHE_FILE=/data/dataflows.json and ABS_LOG_DIR=/data/logs.
docker run -i --rm -e ABS_LOG_LEVEL=info -e ABS_CACHE_REFRESH_HOURS=6 \
ghcr.io/seansoreilly/mcp-server-abs:latestTo build locally instead of pulling:
docker build -t abs-mcp-server .
./scripts/smoke-test.sh abs-mcp-server # drives an MCP initialize over stdioAs a sidecar
Because the transport is stdio rather than a socket, an agent framework runs
the container as a child process and speaks to it over the pipe — it is not a
network service. In Kubernetes or Compose, a true sidecar needs a supervising
process that owns the pipes; the common case is a host process spawning
docker run -i --rm ... per session.
Development
Prerequisites
Node.js 20 or higher (required by the MCP SDK v2 packages)
npm 8 or higher
Building
npm run buildRunning
npm startDevelopment Tools
npm run build: Build the TypeScript codenpm start: Run the servernpm test: Build, then run the test suite (Vitest)npm run typecheck: Typecheck bothsrc/andtests/npm run lint/npm run lint:fix: Biome lint + format checknpm run inspector: Run the MCP inspector for testing
Project Structure
src/
├── index.ts # Process entry point: transport + main()
├── server.ts # buildServer(): tool registration and handlers
├── services/
│ └── abs/
│ ├── ABSApiClient.ts # ABS API communication (not yet wired in)
│ └── DataFlowService.ts # Dataflow caching (not yet wired in)
├── types/
│ └── abs.ts # Type definitions, incl. the ABSError class
└── utils/
└── logger.ts # Winston config; all output to stderr
tests/ # Vitest suite, incl. stdio integration tests
agent_docs/ # Protocol research notesImplementation 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
Winston, configured for an stdio MCP server:
Every level writes to stderr. stdout is the JSON-RPC channel, so a single log line there would corrupt the protocol.
Structured JSON to rotating files under
ABS_LOG_DIR; human-readable to the consoleLevel and directory set via
ABS_LOG_LEVEL/ABS_LOG_DIR
Integration with Claude Desktop
Claude Desktop spawns the server itself, so point it at either the built entry
point or the container. Add one of these to claude_desktop_config.json, then
restart Claude Desktop — the ABS tools appear in the "Available MCP Tools"
window.
Local build (run npm run build first):
{
"mcpServers": {
"abs": {
"command": "node",
"args": ["/absolute/path/to/mcp-server-abs/build/index.js"]
}
}
}Container:
{
"mcpServers": {
"abs": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-v", "abs-mcp-data:/data",
"ghcr.io/seansoreilly/mcp-server-abs:latest"
]
}
}
}API Documentation
For more information about the ABS Data API:
Contributing
Fork the repository
Create a feature branch
Make your changes
Submit a pull request
License
MIT License
Available Tools
1 toolquery_datasetC
Query a specific ABS dataset with optional filters
| Name | Required | Description | Default |
|---|---|---|---|
| datasetId | Yes | ID of the dataset to query (e.g., C21_G01_LGA) |
TDQS
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.
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.
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.
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.
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.
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 tool update
v1.0.0- First observed
query_dataset
TDQS
Scored across 1 tool
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.
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.
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.
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
Related MCP Connectors
Australian Bureau of Statistics (ABS) Data API MCP.
Australian Bureau of Statistics data: 1,227 tables, offering only options confirmed to serve data
UK Office for National Statistics dataset catalogue + Beta JSON API
This MCP server provides seamless access to Malaysia's government open data, including datasets, w…
Related MCP Servers
- AlicenseAqualityAmaintenanceThis server provides access to IBGE's public APIs, enabling AI assistants to query geographic, demographic, and statistical data from Brazil.23847 npm11MIT
- AlicenseAqualityCmaintenanceMIT ABS sister MCP — same five tools, citations. Pair with the ausdata gateway for joins and Embed.7264 PyPI1MIT
- AlicenseAqualityAmaintenanceMCP server for structured Australian macroeconomic and financial data from the Australian Bureau of Statistics (ABS), the Reserve Bank of Australia (RBA), and the Australian Prudential Regulation Authority (APRA).214114 PyPI4MIT
- AlicenseAqualityFmaintenanceCited Australian stats via the ausdata.io gateway — stable AU.* series IDs, source_url + retrieved_at on every response. Free tier. Not a data broker; upgrade for Embed / signed / webhooks.2876 npm2MIT