latest

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

With no annotations provided, the description effectively carries the full burden. It honestly discloses that the tool trims to the most-recent period for transposed tables and returns the same shape as get_data for wide tables, providing key behavioral context beyond the schema.

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 extremely concise: two sentences plus an example, front-loaded with the main action, and every sentence adds 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 existence of an output schema, the description is fairly complete. It explains the behavior for different table types and provides an example. However, it could be more explicit about when to prefer get_data or other siblings.

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 coverage is 100%, so the baseline is 3. The description adds marginal value by providing an example usage and noting that filters are the same as get_data, but most parameter information is already in the schema.

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 returns the most recent observation(s) per measure for a dataset, and it distinguishes itself by explaining how it behaves differently for transposed time-series tables vs. wide single-year tables, implicitly differentiating from get_data.

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 usage through behavioral explanations (e.g., for 'latest' period) and provides an example, but it does not explicitly state when to use this tool versus siblings like get_data, nor does it mention when not to use it.

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