detect_change_points

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the algorithm used (CUSUM) and the nature of the output (points where the mean changes significantly), but it does not explain output shape, edge cases, or interpretation details beyond that.

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 tight sentences with no filler. The core purpose and algorithm are stated up front, and every word contributes to understanding what the tool does.

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 is adequate for a read-only, well-schema-documented tool, but it lacks guidance on when to use it versus the many analytics siblings and does not describe the return format. Since there is no output schema, a bit more return-shape context would improve completeness.

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 all parameters. The description does not add parameter-specific meaning beyond the overall algorithm context, which matches the baseline of 3.

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 ('Detect') with a clear resource ('regime shifts in metric data') and names the algorithm (CUSUM). It clearly conveys what the tool does, though it does not explicitly differentiate it from sibling analytics tools like get_metric_anomalies or detect_seasonality.

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 the tool is for detecting mean shifts in metric data, but it gives no explicit guidance on when to prefer it over alternative analytics tools. There are no exclusions, prerequisites, or references to sibling functions, which is a notable gap given the large sibling list.

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