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

With no annotations, the description carries the full burden. It discloses a significant side effect: writing to Spatial Memory and dropping an audit breadcrumb. However, it states the write unconditionally, while the schema has a write_to_spatial_memory parameter that can disable it, creating a potential inaccuracy.

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 sentences with no redundant phrasing. The verb 'Query' is front-loaded, and the sentence structure efficiently conveys purpose, return data, and side effects.

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 no output schema and no annotations, the description adequately lists the return fields (wind, precipitation, temperature) and side effects. However, it omits details about response format, units, or the optionality of the timestamp, which are only evident from the schema.

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 baseline is 3. The description adds little beyond the schema, except implying the timestamp and H3 cell are key inputs. It does not clarify the write_to_spatial_memory parameter's role, which is mentioned in the description but not linked to the parameter.

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 queries atmospheric conditions for an H3 cell at a timestamp, listing specific return types (wind, precipitation, temperature) and the data source (Open-Meteo ERA5). This distinguishes it from sibling tools like perception_classify or perception_fetch_tile.

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 provides clear context for when to use the tool (query weather for a geospatial cell) but does not explicitly mention alternatives or exclusions. The context is sufficient without being explicit about when-not-to-use.

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