list_heat_pump_models

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

No annotations are provided, so the description carries the full burden. It transparently describes the return structure and filter behavior (e.g., case-insensitive partial match for brand, typical HSPF2 range). No side effects are expected, and the description adequately covers the tool's 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 well-structured with clear sections for overview, return values, and filter details. It is concise but could be slightly tightened (e.g., some redundancy between filter list and explanation). Still, it is efficient and front-loaded.

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?

Given the output schema exists, the description sufficiently explains the return structure (total_models, brands, models). It covers the essential aspects for a listing tool. No major gaps are apparent.

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 baseline is 3. The description adds meaningful context beyond the schema (e.g., 'Case-insensitive partial match' for brand, 'Capacity range for sizing' for BTU, 'typical range: 8.0-12.0' for min_hspf2), enhancing usability.

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 lists heat pump models with optional filters, using a specific verb ('List') and resource ('heat pump models'). It distinguishes from siblings like calculate_heat_pump_sizing by focusing on retrieval rather than calculation.

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 explains when to use the tool and details each filter with examples. While it doesn't explicitly state when not to use it, the sibling context provides differentiation. The filter descriptions are clear and actionable.

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