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

With no annotations, the description takes full responsibility. It explains the return of most recent observations and the difference in output for transposed vs wide tables. No contradictions; all behaviors are accurately described.

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: one introductory sentence, a short technical note, and two code examples. Every sentence is essential, well-organized, and front-loaded with the core purpose.

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 has an output schema and high schema coverage, the description sufficiently covers behavior and includes examples. It addresses different dataset shapes, but could elaborate on edge cases or error states. Still, it is complete for typical use.

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 baseline is 3. The description adds minimal extra meaning beyond the schema's explicit examples and descriptions. It reinforces parameter roles but does not significantly deepen understanding.

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 'Return the most recent observation(s) per measure for a dataset', identifying the verb and resource. It distinguishes itself from sibling tools like get_data by explaining behavior for transposed vs. wide tables, and provides specific examples.

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?

It clarifies when to use the tool (for latest observations) and contrasts with get_data via table-type behaviors. Missing explicit exclusion clauses or direct alternative recommendations, but the context is clear enough.

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