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

With no annotations, the description carries the full burden of explaining behavior. It discloses the data source and storage context (Open-Meteo/ERA5 via bordeaux.guru) and the three return modes: summary, monthly, and daily. It stops short of describing edge cases, season boundaries, units, or error behavior, but the key behavior is clearly presented.

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 dense but front-loaded: it states the core purpose first, then lists relevant metrics and return modes. The parenthetical source explanation is useful, and no sentence is wasted, though the combined clause is somewhat packed.

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?

The description covers the main use case, parameter context, and return granularity, which is adequate for a basic climate retrieval. However, with no output schema, an agent still lacks detail about exact season date ranges, units of measurement, valid appellation values, and behavior for unavailable data.

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 appellation, year, and granularity. The description's mention of summary, monthly, and daily granularities largely mirrors the schema, adding little independent meaning beyond context.

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 identifies a specific resource: growing-season climate for one Bordeaux appellation and year, and lists the data types it returns. It does not, however, explicitly contrast itself with sibling tools like compare_climate, so sibling differentiation is only implicit.

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 phrase 'for a Bordeaux appellation and year' implies the intended call for a single appellation/year climate query. The description gives no explicit guidance on when not to use it or when to prefer a sibling such as compare_climate.

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