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

Annotations already cover readOnly/idempotent/non-destructive/openWorld, so the bar is lower. The description adds the return payload shape, which is valuable since there is no output schema, but it omits what happens for an unknown or unpublished seriesId.

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?

Three compact sentences: payload shape first, then worked examples, then the sibling routing hint. No filler.

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?

For a single-parameter read tool with no output schema, the description supplies the return shape and usage examples, which is nearly everything needed. Only failure/unpublished-series behavior is left unstated.

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% and the schema already carries the same example IDs used in the description, so the parameter meaning is fully documented in structured data. The description adds no format or validation detail beyond that baseline.

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?

States a specific verb+resource ('most recent observation for a single series') and the exact return shape {date, value}. It even distinguishes itself from the policy_rate sibling by naming it and the extra data it returns.

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?

Gives a clear conditional redirect: for the policy rate, policy_rate also returns the previous rate and change timing. It does not compare against get_observations or list_series, so the when-to-use picture is clear for one sibling but not exhaustive.

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