convert_temperature

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 mentions 'validation and formulas,' which hints at input validation and mathematical operations, but doesn't disclose critical behavioral traits like error handling, precision, rounding, or output format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 front-loaded, stating the core purpose in a single sentence. The domain/category tags add context without verbosity. However, it could be more structured by separating usage notes from the main purpose, but it's efficient overall.

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 no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It lacks details on behavior, parameters, return values, and error handling. For a tool with 3 required parameters and mathematical operations, more context is needed to ensure proper usage by an AI agent.

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 3 parameters with 0% description coverage, and the tool description doesn't add any parameter-specific information. It doesn't explain what 'value', 'from_scale', or 'to_scale' mean, their expected formats (e.g., string values like 'Celsius'), or constraints. With low schema coverage, the description fails to compensate, leaving parameters undocumented.

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: converting temperature between Celsius, Fahrenheit, and Kelvin scales. It specifies the action ('convert') and resource ('temperature'), and mentions validation and formulas. However, it doesn't explicitly differentiate from sibling tools like convert_angle or convert_length, though the domain/category tags help.

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 no guidance on when to use this tool versus alternatives. It mentions the domain and category, but doesn't specify prerequisites, constraints, or compare it to other conversion tools in the sibling list (e.g., convert_angle, convert_area). Usage is implied by the name and description alone.

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