Blv Get Animal Health Stats

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

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so the safety profile is covered. The description adds the data source and format (opendata.swiss CSV/JSON), which is modest extra context, but says nothing about refresh cadence, coverage limits, or rate/auth behavior. With annotations carrying the safety burden, this is adequate but not rich.

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 short sentences, front-loaded with the resource and followed by the use case. No padding, though the second sentence is thin enough that it borders on 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?

An output schema exists, so return values need not be explained, and annotations cover the mutation profile. The remaining gap is sibling differentiation and any note on data scope/cadence, which an agent in a crowded blv_* toolset would need.

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% and the single 'year' parameter is fully documented in-schema ('None returns all available years'). The description adds no parameter detail beyond that, 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 resource ('annual animal health statistics from BLV') with a clear source attribution. However, it does not distinguish itself from several sibling tools that also return animal-health data (blv_get_avian_influenza, blv_get_meat_inspection_stats, blv_get_antibiotic_usage_vet), so an agent cannot tell from the description alone whether this is the aggregate overview or a subtopic.

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 'Use case: track year-over-year animal health indicators across Switzerland' line implies when the tool is useful, but it gives no explicit when-not and names no alternative sibling for narrower topics. Selection guidance is left to inference.

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