Get anomalies

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description reinforces that the tool is read-only and adds behavioral context: 'A healthy machine returns no anomalies.' It also outlines the return structure, which is consistent with annotations. No contradiction.

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 with two short paragraphs. The first sentence states the purpose, followed by input details and return structure. Every sentence adds value, and there is no 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?

The description covers the tool's purpose, parameters, and return structure. With an output schema present, the explanation of return values is sufficient. However, it could briefly explain how step_ms affects anomaly detection granularity, so it is not fully complete.

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 coverage is 100%, so the baseline is 3. The description repeats defaults and options already present in the schema (e.g., device_id optional, step_ms default 30000, response_format). It adds minimal extra meaning beyond the schema, so a score 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 clearly states the tool detects threshold crossings for temperature or vibration over a time window. It lists inputs and output structure, and the verb 'Detect' with resource 'anomalies' is specific. It distinguishes from siblings like list_devices and get_telemetry which serve different purposes.

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 explains when to use the tool (detect anomalies) and implies that healthy machines return no anomalies. However, it does not explicitly state when not to use it or mention alternatives like get_telemetry for raw telemetry data. Still, the context is clear.

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