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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds valuable behavioral context about how the algorithm works: 'Early windows define normal, recent windows are tested for anomalies,' and explains the training/test split, which goes beyond the annotations.

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 front-loaded with the core purpose and then provides a structured workflow and examples. It is somewhat long, with four similar examples that could be condensed, but each example serves to illustrate a different data source. Overall it is efficient and well-organized for the tool's complexity.

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?

For a tool with 4 parameters, 100% schema coverage, and no output schema, the description is remarkably complete. It explains the algorithm, gives a typical workflow, and states the return value ('which time windows are anomalous'). It also provides multiple example scenarios, making it clear how to apply the tool in practice.

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%, so the schema already documents every parameter thoroughly. The description adds minimal new meaning beyond mentioning the array and window size; the training/testing semantics are already in the test_windows schema description. Thus, it meets the baseline but does not elevate it.

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 states 'Detect anomalies in time-series data' with a clear verb and resource. It provides concrete examples (revenue, stock, server health) that differentiate it from generic scan tools like waveguard_scan, but it does not explicitly name alternatives or contrast with siblings.

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 gives explicit usage context: 'use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns.' It outlines a typical workflow and multiple use cases, but does not mention when not to use the tool or name alternative tools.

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