energy_find_pv_installations

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

The annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond these: the data source ('Quelle: ch.bfe.photovoltaik-grossanlagen') and the scope limitation (only large-scale systems are included). However, it does not disclose additional traits such as pagination, ordering, or behavior when no results are found.

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 concise and well-structured using XML-style elements (use_case, important_notes, example). It front-loads the primary purpose, followed by contextual guidance and a concrete example. Every sentence contributes meaningful information without 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 the purpose, use case, important scope notes, a parameter example, and the source of data. It also mentions output fields (Leistung, Jahres- und Winterproduktion, Projektstatus). With an output schema present, the description is sufficiently complete for an agent to understand what the tool does and when to use it.

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 description does not explain each parameter, but the example 'lat=46.2, lon=7.5, radius_m=40000' provides a concrete usage pattern. The input schema itself contains detailed descriptions for lat, lon, and radius_m, including bounds and defaults, so the example supplements the schema without repetition. Given schema_description_coverage is 0%, the description only partially compensates with the example.

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's purpose with a specific verb+resource: 'Sucht Photovoltaik-Grossanlagen' (finds PV large-scale systems). It also lists key output fields (Leistung, Jahres- und Winterproduktion, Projektstatus) and explicitly distinguishes from sibling tools via the important_notes, noting that individual PV installations are found in energy_find_power_plants.

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 element gives clear context for when to use the tool ('Recherche zu PV-Grossprojekten, Energieplanung'). The important_notes explicitly states a limitation and an alternative: 'Erfasst nur Grossanlagen — Einzel-PV erscheint in energy_find_power_plants', providing a when-not-to-use condition and naming the alternative tool.

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