get_source_breakdown

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 richly: overlapping per-source counts that do not sum to the total, the meaning of quality_filter, estimated recall ~0.6, and the model-estimated nature of kwp/surface/tilt/azimuth. These are exactly the caveats an agent needs to avoid misinterpreting 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?

Front-loaded with the purpose, then layered caveats, all of which earn their place. Minor redundancy: get_data_quality_reference is referenced twice (once for source_encoding, once as a general recommendation), which slightly dilutes the ending.

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 single-parameter aggregation tool with no output schema, the description is complete: it explains the output categories, the non-additive nature of counts, the extra 2+ source figure, and the dataset's quality limits. An agent has everything needed to call it and interpret the result responsibly.

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% with a single parameter, so the baseline is 3; the schema already documents dpt and the omit-for-all-France behavior. The description reinforces the scope but adds no syntax or format detail beyond the schema, so no lift above baseline.

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+resource (break down detections by source code) and immediately defines the scope: one département or all of France. The parenthetical decoding of the source codes lets an agent understand the output domain without opening any other tool, and it is clearly distinct from siblings like get_source_reference or get_field_distribution.

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 clear usage context (source counts overlap, quality_filter semantics) and explicitly routes the agent to get_data_quality_reference before assessing fitness. It stops short of naming when NOT to use it versus siblings such as count_detections or get_source_reference, so it is strong context without full alternative routing.

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