get_field_distribution

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

With no annotations, the description carries the full burden and does so well: it discloses that kwp/surface/tilt/azimuth are model estimates rather than surveyed values, that first_seen/last_seen are imagery years and not installation dates, that the dataset is a detection dataset with ~0.6 recall, and that a missing detection does not imply no PV exists. It also spells out the exact semantics of quality_filter=true (2+ sources) and cross-references source_encoding.

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 purpose and scope are front-loaded, and most sentences carry distinct caveats. However, the model-estimate warning for kwp/surface/tilt/azimuth is stated twice in near-identical form ('are model estimates' then 'are model estimates, not surveyed values'), which is wasted repetition in an otherwise tight structure.

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 and no annotations, the description still conveys the return shape (exact counts per bin), the geographic scope, the year-range handling, and the key fitness-for-use caveats, plus a pointer to the quality reference. It is nearly complete, though it could say more about bin behavior or expected response structure for first_seen/last_seen.

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%, so the baseline is 3. The description adds genuine semantic meaning beyond the schema: it flags which enum values are model estimates, interprets first_seen/last_seen as imagery years, and reinforces the 2+ source meaning of quality_filter. It does not, however, clarify dpt format or bin_edges behavior beyond what the schema already says.

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 opening clause gives a specific verb+resource+output shape: a histogram of a named numeric field with exact counts per bin, scoped to France or a single département. The enumerated field list and the quoted user question make it unmistakable against siblings like get_field_coverage or get_department_yearly_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?

It states the question the tool answers ('how are installations distributed by size / tilt / orientation / year?') and routes the agent to get_data_quality_reference before assessing fitness for a use case. It gives no explicit when-not-to-use or a named alternative for the same job, so it falls short of a 5.

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