get_environment_anomaly

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

With no annotations, the description adds meaningful behavioral context: it is a read-only observation (implied by 'Observe'), de-identified with k-anonymity >= 10, and costs DAC. This provides transparency beyond the schema.

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?

Two concise sentences clearly convey purpose, scope, privacy, and cost. There is no redundancy or filler, making it easy to parse.

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 core functionality, privacy guarantees, and cost. While it does not detail the exact response structure, the mention of 'counts per dimension' gives an adequate mental model absent an output schema.

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 description does not need to explain parameters. The tool description slightly augments the 'dimension' parameter by describing the output shape, but adds no critical semantics beyond the schema.

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 uses a specific verb 'Observe' with a clear resource: environment-level anomaly distribution, detailing the output as within_band/outlier counts per dimension across the population. This distinguishes it from decision-level or pattern-level tools.

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?

No guidance is given on when to use this tool instead of alternatives like observe_environment or compare_anomaly. It mentions costs and k-anonymity, but does not provide usage context or exclusions.

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