get_department_yearly_stats

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

With no annotations, the description carries the full burden and does substantial work: it discloses the dataset is detection-based with estimated recall ~0.6, warns that missing detections are not evidence of absence, and flags kwp/surface/tilt/azimuth as model estimates rather than surveyed values. It also explains the quality_filter=true semantics (2+ source detections), which is server-level behavior not captured in the schema. It omits response shape and any pagination/aggregation granularity.

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?

Front-loaded with the core purpose before the caveats, and each sentence adds distinct value (data quality, recall, estimate caveat, quality_filter, pointer to reference tool). It is on the long side and the get_data_quality_reference pointer appears alongside an already-implied caveat, so it is slightly more verbose than needed.

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 one-optional-parameter read tool with no output schema and no annotations, the description supplies the caveats an agent needs to interpret results responsibly (detection vs inventory, model estimates, recall). It could say more about what the returned counts/capacity look like and at what granularity, but the critical fitness-for-use context is present.

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 single optional dpt parameter is fully documented in the schema, including the 'omit for all' behavior. The description adds no syntax or format detail about dpt, so baseline 3 applies; the 'based on first-seen imagery year' phrasing adds temporal framing but no parameter-level meaning.

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 and resource (yearly system counts and capacity by département) and pins the temporal basis to first-seen imagery year, which is a meaningful scope detail. It does not explicitly differentiate itself from the closest sibling, get_department_capacity_stats, so the agent must infer the distinction between 'yearly stats' and 'capacity stats'.

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 use case (tracking apparent PV deployment growth over time) and a prerequisite workflow: consult get_data_quality_reference before judging fitness for a specific use case. It stops short of stating when to prefer this over get_department_capacity_stats or get_department_profile, so the routing guidance is incomplete.

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