anomaly_dbscan

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

With no annotations, the description carries full burden. It discloses unsupervised nature, density clustering, and rolling window. However, it omits specifics on output format, rate limits, or side effects. Does not contradict annotations as none exist.

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 sentences efficiently convey the tool's method, scope, and uniqueness without superfluous content.

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?

Given no output schema and no annotations, the description is adequate but incomplete. It explains purpose and method but does not describe return values, error cases, or prerequisites, leaving some gaps for an agent.

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% (country_code has description). The tool description does not add any additional meaning beyond what the schema provides, so baseline score 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?

Description clearly states it uses DBSCAN for unsupervised anomaly detection on OONI days per country, with a rolling 45-day window. It distinguishes from sibling tools by positioning itself as a second-opinion signal complementing a labeled classifier.

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 as a complementary tool to a labeled classifier but does not explicitly specify when to use this tool versus other anomaly detection siblings like anomaly_score or anomaly_seasonal. No when-not-to-use or alternatives are provided.

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