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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lower. The description still adds real value beyond them: the detection method, the 'biggest first' ordering, and the fact that each result carries its 'likely causes' — a return-content trait the annotations say nothing about.

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?

A single sentence that front-loads what the tool detects, then the ordering, then the payload. No filler, nothing wasted.

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 read-only analytical tool with no output schema and full parameter documentation, the description covers the essential ground: what counts as a spike, ordering, and the inclusion of causes. It stops just short of helping an agent decide between this and explain_spike.

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% and each of the three parameters (limit, range, domain) is fully documented in the schema, including defaults and bounds. The description adds nothing about parameters, so the baseline 3 applies.

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?

States a concrete verb-resource combination ('Detected traffic jumps') and defines the detection criterion precisely: hourly pageviews far above the same hour in prior weeks. It implicitly separates itself from raw-view siblings like get_timeseries by framing the output as anomalies with 'likely causes', but it never names an alternative explicitly.

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?

There is no when-to-use, when-not-to-use, or prerequisite guidance. With a sibling named explain_spike sitting right next to it, the definition leaves the relationship between detecting a spike and explaining one entirely to inference.

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