Predict maintenance

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

The description explains that the tool forecasts risks and returns ranked recommendations, but does not disclose behavioral traits such as whether it is read-only (likely, but not stated), computational cost, or data prerequisites beyond the VIN. Without annotations, the description carries full burden and provides only basic 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 extremely concise at two sentences, front-loading the key action ('Forecast component-level maintenance risks'), and every word adds value. No redundancy or filler.

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 single parameter, no annotations, and no output schema, the description provides a clear purpose and output expectation. However, it lacks details about the output structure (e.g., what fields are in the ranked risks) and any limitations. Still, it is sufficient for an agent to understand core functionality.

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 description coverage is 100% (the 'vin' parameter is described as 'Vehicle VIN'). The tool description adds contextual information about inputs (signals, DTCs, domain scores) but does not add meaning beyond the schema for the parameter itself. Baseline of 3 is appropriate.

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 uses the specific verb 'Forecast' and clearly identifies the resource as 'component-level maintenance risks'. It also specifies the inputs (current signals, DTCs, domain scores) and output (ranked risks with recommendations), distinguishing it from sibling tools like get_dtcs or get_vehicle_health which provide current data rather than predictions.

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 it should be used for predicting future maintenance risks based on current data, but does not explicitly state when to use this tool versus alternatives (e.g., get_vehicle_health for current state, get_dtcs for diagnostic codes). No exclusion criteria or usage context is provided.

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