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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description goes beyond them by disclosing the return format (CSV date,value) and the candidate-list fallback on ambiguous input. This is meaningful behavioral context the annotations do not provide.

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?

Two tight sentences that lead with the core action and immediately cover the two possible response shapes. No filler, nothing redundant.

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?

With no output schema, the description usefully explains both possible return shapes (CSV data vs. JSON candidates), which is enough for correct invocation. It stops short of describing pagination or how large the returned CSV might be.

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 100%, so the schema already documents series_id, from, and to thoroughly, including typo tolerance and the date format. The description adds nothing beyond what the schema states, so the baseline 3 applies.

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 (Get) and resource (macroeconomic data) scoped to 'a specific series', which clearly separates it from sibling listing-style tools. However, it never names or contrasts with get_macro_series, the closest sibling, leaving the boundary between 'data' and 'series' to inference.

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 ambiguity flow ('ask the user to pick one and call again') gives implied operational guidance, but there is no explicit statement of when to use this tool versus get_macro_series, get_index_data, or other data endpoints. Usage is inferable but not stated.

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