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

With no annotations, the description carries the full disclosure burden, and it delivers: live verification against ABS, the guarantee that successful calls return real data, row caps/defaults, response contents (summary and URL), and invalid-selection behavior. This goes well beyond what the schema alone provides.

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?

Five purposeful sentences move from core purpose to selection semantics, verification, output behavior, and error handling. Every sentence earns its place, and there is no filler or unnecessary repetition of schema fields.

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?

The description is behavior-rich and, combined with an output schema, covers most of what an agent needs for a straightforward fetch. The notable gap is that the required `table` parameter is left unexplained and sibling tools are not referenced for discovering valid table identifiers or options.

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 real meaning for `select` (codes or labels, multiple allowed, omitted dimensions match all) and clarifies default period/maxRows behavior. However, schema coverage is only 57%, and the required `table` parameter plus `firstN`/`endPeriod` are not meaningfully described, so the compensation is incomplete.

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 opens with 'Fetch observations,' a concrete verb and resource, and immediately establishes the ABS data context. This makes it easy to distinguish get_data from the search/describe siblings, even though no explicit sibling names are used.

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 ABS observation context and selection semantics imply this is the tool for pulling data once a dataset is known, but it never explicitly states when not to use it or routes to search_tables/search_options for finding valid tables or options. The 'asked to choose from the valid ones' hint suggests an iterative workflow but does not name alternatives.

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