energy_solar_potential

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds critical behavioral details: the internal radius is raised to at least 2000m, small radii may yield no results, and suitability classes are numbered 1-5. These go beyond annotation metadata and clarify real-world behavior.

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 description is compact and front-loaded with the core statement. The use of structured tags for use_case, important_notes, and example makes it highly scannable. Every sentence contributes unique value with no redundancy.

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?

The description covers purpose, use case, data source, radius behavior, and suitability classes. Since an output schema exists, the description does not need to explain return values. It is fully adequate for a spatial query tool of this complexity.

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?

The input schema fully documents lat, lon, and radius_m with ranges and defaults. The description adds a concrete example (lat=47.37, lon=8.54, radius_m=3000) and a note about the radius being internally raised to 2000m, enhancing the semantic understanding of the parameters beyond the schema alone.

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 clearly states the tool queries the solar suitability of roofs for a location ('Fragt die Solareignung von Dächern für einen Standort ab'). It is specific with verb and resource, and the use case distinguishes it from sibling tools about power plants, wind turbines, and other energy infrastructure.

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 explicitly describes when to use this tool (deciding on PV systems on school/public buildings, solar potential analyses). It does not name alternative tools or exclusion criteria, but the provided context is sufficient for basic usage decisions.

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