get_observations

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

No annotations are provided, so the description fully carries the burden. It discloses that the tool returns observations plus the unit of measure, and explains the wildcard behavior. It does not mention any destructive actions, which is appropriate for a read operation.

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 concise with only three sentences, each serving a clear purpose: stating what the tool does, explaining usage of dimensions, and noting return value. No redundant information.

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 presence of an output schema, return values are covered. The description adequately explains the main behavior and the critical dimensions parameter. With 4 required params and no annotations, it is fairly complete but could briefly mention the other parameters.

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 0%, so description must add meaning. It explains the dimensions parameter thoroughly (exactly one code per dimension, wildcard allowed). However, dataset_id, edition, and version are not described beyond their names, leaving some ambiguity for new users.

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 the tool retrieves observations for a dataset version, with specific verb 'Retrieve' and resource 'observation(s) for a dataset version'. It distinguishes from siblings by focusing on the observation retrieval and wildcard usage, which is unique among the listed siblings.

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 provides explicit guidance on how to use the dimensions parameter, including the wildcard '*' for fetching entire time series. It implies when to use (retrieving observations) but does not explicitly state when not to use or compare to alternatives like get_timeseries.

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