waveguard_scan_timeseries

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

With no annotations, the description carries full burden. It discloses GPU acceleration, automatic overlapping windows, training on first N windows, and returns per-window anomaly scores, confidence, and p-values. It does not mention potential side effects (none expected) or resource usage, but covers core behavior well.

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 two paragraphs: first explains purpose and algorithm, second gives an example. It is well-structured and efficient, though the example could be slightly more compact. No extra fluff.

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?

No output schema exists, so description must explain returns. It lists 'per-window anomaly scores, confidence, and p-values' – sufficient for an agent. It also covers input, algorithm, and example. Given tool complexity, this is 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 has 100% description coverage, so baseline is 3. The description adds significant value: explains window_size gives finer resolution, test_windows defaults to last ~40%, sensitivity range 0.5-5.0, and provides a concrete example linking parameters. This is exemplary.

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 anomalies in time-series data using GPU-accelerated wave physics simulation. It specifies the input (flat numeric array, window size) and explains the algorithm. It distinguishes itself from siblings like waveguard_scan by focusing on time-series analysis.

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 (detect anomalies in time-series) and provides a concrete example (CPU-usage readings with window_size=10). It does not explicitly mention when not to use or alternatives, but the context is clear enough for agents.

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