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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose important outcome traits: it returns a device family, honest physics, appropriate room sizing, and a matching guide. However, it does not describe the exact response shape, edge cases, or whether the tool performs any side effects; for a recommendation-style tool, this is a reasonable but not rich level of transparency.

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: one bilingual purpose sentence plus a sibling-relation sentence. It front-loads the core purpose and avoids filler while still conveying the problem scope, output content, and hierarchy among tools.

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, the description still conveys the essential result: a device-family recommendation with physics rationale, sizing, and guide. Parameter semantics are fully covered by the schema, and the sibling relationship is stated. A more explicit return format would improve completeness, but for a two-parameter selector this is sufficient.

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%, and the schema already provides meaningful descriptions for both parameters, including the problem enum values and qm range/default. The description only loosely reinforces 'right size for the room' and lists the same problem categories, so it adds little semantic value beyond the schema. Baseline 3 applies.

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 concrete goal: 'Which device family solves a given indoor-climate problem?' and enumerates the problem categories (too hot, damp/mould, too cold, stale air). It also specifies the value-adds (honest physics, correct room sizing, matching guide). It explicitly distinguishes itself from related sizing tools by naming btu_empfehlung/heizleistung_watt as the layers below it.

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 gives clear context: use this tool when the user has an indoor-climate problem and needs a device family recommendation before technical sizing. It positions the tool as 'the decision layer above' btu_empfehlung/heizleistung_watt, implying a sequence. It lacks explicit when-not-to-use or alternative-conditions, but the context is not ambiguous.

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