predict_maintenance_window

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. The verb 'predicts' implies a read-only analysis, but the description does not explicitly state that no data is modified, nor does it describe output format, error conditions, or any side effects. This lack of explicit transparency is a significant gap for a tool with no annotation support.

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 a single concise sentence of 11 words, with no redundant or filler content. It is well-structured and front-loaded with the core purpose. Every word contributes to the meaning, making it highly concise.

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?

With no output schema, no annotations, and incomplete parameter coverage, the description does not provide enough context for a complete understanding. It does not explain what the prediction output looks like (e.g., probability, time window, binary outcome) or the meaning/usage of parameters. The tool is simple, but the description leaves key details unspecified.

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 0%, and the description fails to mention or explain any of the two required parameters (machine_id, sensor_history_hours). The phrase 'live sensor vibration from database' hints at sensor data but does not map to the parameters. The description provides no context for what these parameters mean or how they are used.

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 action ('Predicts machine failure') and specifies the data source ('live sensor vibration from database'). This specific verb+resource combination clearly distinguishes it from the sibling tools, which handle sensor normalization, flow rerouting, energy scheduling, parameter adjustment, and compliance auditing. Although the tool name references 'maintenance window' and the description says 'machine failure', the intent is clear and unambiguous.

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 offers no explicit guidance on when to use this tool versus alternatives. It does not describe any preconditions, exclusions, or alternative tool references. The usage is implied by the purpose statement but not elaborated, leaving the agent without clear decision context.

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