get_valuation_factors

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

The description adds a key behavioral trait by stating 'Costs 12 credits per call; failed calls are auto-refunded,' which is not present in annotations (none provided). It does not explicitly declare the operation as read-only or discuss side effects, but for a quantification tool this is likely safe. The cost and refund information contributes some transparency, though it omits other potential details like rate limits or required auth.

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 consists of two concise sentences: the first clearly states the purpose, and the second provides cost and refund behavior. There is no redundant or filler content. The structure is front-loaded with the core function, making it easy to parse quickly.

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 presence of an output schema, the description does not need to detail return values. It covers the tool's purpose and cost/refund behavior. It does not explicitly mention the required input parameters (make/model/year), but the input schema provides that context. The description is short but sufficient for the agent to understand when and why to invoke this tool, though it could be slightly more complete by noting the need for make/model/year.

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 for make, model, and year, and the description does not explain these parameters. It only says 'for this car' without specifying how to provide the car details. Since schema coverage is 0%, the description carries the burden to clarify parameters, but it fails to do so, leaving the agent to infer that make/model/year are the identifiers. The parameters are self-explanatory, but the description adds no semantic value beyond what the property names imply.

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 function: 'Quantify the price impact of import status, gearbox type, and fuel type for this car.' This uses a specific verb (quantify) and resource (price impact of specific factors), distinguishing it from siblings like get_market_valuation which provides overall valuation. It is immediately understandable what the tool does.

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 implies usage context through its focus on factor-specific impact analysis, suggesting it is used when detailed factor contributions are needed rather than overall market value. However, it does not explicitly state when to use this tool versus alternatives like get_price_history or get_regional_pricing, nor does it mention any exclusions. This is implied usage but lacks direct guidance.

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