get_data

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

With no annotations provided, the description carries full behavioral disclosure burden. It reveals two input forms (dotted vs split), period format conventions, and an example, but it omits details on response format, error handling, or explicit read-only confirmation. The disclosure is useful but not comprehensive for a mutation-capable-looking accessor.

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 three sentences plus one example: opens with the purpose, then gives usage guidance, then period formats and an example. Every sentence earns its place without redundancy or fluff, making it concise and well-structured.

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 6 parameters, a nested filters object, no output schema, and no annotations, the description provides essential guidance for constructing a query, including input forms and period formats. It does not explain return format or error behavior, but for a generic accessor with a straightforward filter-and-period interface, it is reasonably complete for a first successful call.

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 input schema already covers all 6 parameters with descriptions and examples (100% coverage), so baseline is 3. The description adds value by explaining the relationship between dataset_id and source (dotted vs split), period format rules depending on dataset, and a concrete usage pattern with filters, going beyond schema repetition.

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 clearly states 'Generic accessor for any curated Australian government dataset', using a specific function (accessor) and resource. It explicitly references search_datasets for discovery and distinguishes itself from specialized sibling tools by being generic and dataset-agnostic.

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?

Provides explicit sequencing: 'Use search_datasets first to find the dataset_id, then call this with filters.' This gives clear context on when to use the tool. However, it does not explicitly name alternatives or state when to prefer specialized tools over this generic accessor, leaving a minor gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.