get_abs_dataset_structureGet ABS SDMX dataset dimensions and codelists.
| Name | Required | Description | Default |
|---|---|---|---|
| dataflow_id | Yes | Non-empty dataset or table id. |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Resolved ABS data structure identifier. | |
| dimensions | No | Dataset dimensions with positions and allowed values. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds no additional behavioral context such as return size, error conditions, or dependencies, but with the annotations present, the bar is lower and a neutral score is appropriate.
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 concise sentence that front-loads the core action and resource. Every word adds value, with no redundant or filler content.
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 simplicity (one parameter, output schema present, clear annotations), the description is largely sufficient. It could be more complete by noting that the structure is returned in SDMX format, but the output schema and purpose cover most needs. Minor gap in not hinting at the tool's relationship to other ABS-specific tools.
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?
Schema description coverage is 100%, with dataflow_id described as 'Non-empty dataset or table id.' The description adds no extra meaning beyond the schema's own parameter description, so the baseline score of 3 applies.
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 uses a specific verb 'Get' and clearly identifies the resource 'ABS SDMX dataset dimensions and codelists'. This distinguishes it from sibling tools like get_abs_data (which presumably returns data) and describe_dataset (which may describe datasets generically).
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 implies the tool is for obtaining structural metadata for an ABS dataset, but it does not explicitly state when to use this versus alternatives or provide exclusions. No comparison to sibling tools or guidance on prerequisites beyond the required dataflow_id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear source or function boundaries (ABS, RBA, APRA, derived series, curated concepts, discovery). However, get_derived_series and get_economic_series both retrieve time-series data and could be confused; list_catalogue and search_datasets also overlap in discovery. The descriptions help, but these pairs require careful reading.
All tools follow a consistent lowercase verb_noun pattern with underscores (describe_, get_, list_, search_). Verbs are used predictably: get for retrieval, list for enumeration, search for discovery. No mixed conventions or camelCase.
14 tools is on the higher end but appropriate for a multi-source economic data API covering ABS, RBA, and APRA. The inclusion of a deprecated alias (list_rba_tables) adds slight redundancy, but the overall count is justified by distinct operations.
The surface covers discovery (search_datasets, list_catalogue), metadata (describe_dataset, structure), retrieval (source-specific and curated), and convenience wrappers (latest/top observations). Minor gaps include lack of a unified structure tool for RBA/APRA and no bulk download option, but core read-only workflows are well covered.