sw_iot_convert

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 discloses the underlying formula (IoT = 1/(1-load)) and explicitly states that the equivalent load is an interpretive conversion, not a representation of actual scheduling – a valuable caveat. It also warns against mixing input sources, providing behavioral context that a mere schema would not.

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 but well-structured, using a lead sentence followed by three bullet points for the usage modes and a final note on the mathematical relationship. Every sentence adds relevant information, avoiding 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?

For a tool with conditional parameter groups and no annotations, the description covers the essential scenarios, formula, and a critical caveat about interpretation. It does not specify behavior when conflicting parameter groups are supplied, but the output schema likely covers return values.

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 has zero description coverage, so the description must compensate. It explains the role of each parameter: sinr_db+sir_db compute IoT, iot_db is graded and converted to load, and load reverses to IoT. It also notes the requirement for consistent input sources, adding semantic meaning beyond the parameter names.

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 opens with a clear verb-resource pair ('IoT 相关的换算与分级') and then enumerates three specific usage modes, distinguishing this conversion utility from sibling tools by its dedicated focus on IoT-related calculations. The purpose is unambiguous and well-scoped.

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 provides explicit instructions on which parameter groups to supply for each of the three computation modes, including a constraint that sinr_db and sir_db must come from the same geometric SINR calculation. It does not mention alternative tools or when not to use this tool, so it falls short of a full 5.

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