anomaly_seasonal

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

With no annotations, the description carries the full burden. It explains the decomposition method and that it flags anomalous residuals. However, it omits details like data requirements, output format, or any assumptions (e.g., sufficient historical data). The description is transparent about the algorithm but lacks practical behavioral context.

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, well-structured sentence that front-loads the core purpose and method. It is concise with no wasted words, earning a top score.

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 tool is simple with one parameter and no output schema. The description explains the algorithm and purpose adequately for a basic understanding, but it does not mention what the output looks like (e.g., a list of anomalous dates). Some users might need more detail on how to interpret results.

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 schema covers 100% of parameters, with a clear description of country_code. The tool description adds context that the country is used for seasonal analysis, but doesn't add extra semantic meaning beyond the schema. Baseline 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 identifies the tool's function: seasonal anomaly detection for a country using STL decomposition. It specifies the method (STL) and what it flags (anomalous residuals after removing seasonal cycles), distinguishing it from sibling anomaly tools like dbscan or domain drift.

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 for seasonal data by mentioning 'removing normal weekly/seasonal cycles', but it does not explicitly state when to choose this tool over alternatives like anomaly_dbscan or anomaly_fused. No usage exclusions or alternative recommendations are provided.

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