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

With no annotations, the description carries the full disclosure burden and meets it: it reveals that the result is a live sum capped by max_rows, and discloses two non-obvious data traits — estimated recall ~0.6 with missing detections not implying absence, and kwp/surface/tilt/azimuth being model estimates rather than surveyed values. This materially prevents misinterpretation of results.

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?

Every sentence earns its place: core purpose, sibling contrast, worked example, then data-quality caveats. The main statement is front-loaded, and the length is justified by the tool's complexity and the absence of annotations and output schema.

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 an 8-parameter aggregation tool with no annotations and no output schema, the definition is remarkably complete: it covers what is computed, how to filter, the truncation cap (with schema detail), output components (quality_summary, total_kwp, truncated), and essential data caveats, and points to get_data_quality_reference for the full quality picture.

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 value on top by grouping the eight parameters into filter categories (département, commune, capacity range, cross-validation, vintages) and by running a worked example that maps 'confirmed by at least two sources' to cross_validated=true, which helps an agent translate a request into parameter values.

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 states a specific verb and resource ('Compute the total estimated installed capacity (kWp) and count of detections') and enumerates the supported filters. It also differentiates from get_department_capacity_stats by contrasting a live filtered subset with a fixed pre-computed aggregate, so an agent can distinguish the siblings at a glance.

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 explicitly names the alternative get_department_capacity_stats and the condition that selects it (fixed pre-computed aggregate with no filters vs live filtered sum), provides a concrete natural-language-to-parameter example (dpt="33", cross_validated=true), and directs the agent to get_data_quality_reference before assessing fitness for a use case.

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