did_it_change

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

With no annotations, the description carries full burden. It discloses key behavioral traits: detection of both rises and drops, use of historical data, a false-alarm rate, and no distribution assumption. This is fairly transparent, though it doesn't clarify whether the tool is read-only or how the false-alarm rate is set.

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 a single, well-structured sentence that front-loads the core question. It is concise and efficient, though could be slightly more scannable with a brief list. Every word earns its place.

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 tool has no output schema, and the description fails to specify what the tool returns (e.g., boolean, p-value, signal strength). This is a significant gap, especially for a statistical tool where output interpretation is critical. The description also doesn't explain how the false-alarm rate is configured or communicated.

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 coverage is 100%, so baseline is 3. The description adds algorithm context (false-alarm rate, no distribution assumption) but does not elaborate on parameter syntax beyond what the schema provides. No parameter-specific details are added, so it meets but does not exceed the baseline.

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 clearly states the tool's purpose: determining whether a metric change is real or noise. It specifies 'detects both rises AND collapses' and includes unique algorithm properties (false-alarm rate, no distribution assumption), distinguishing it from siblings like ab_test (hypothesis test) or which_metrics_matter (metric selection).

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 should be used when you need to know if a change is noise, providing context like 'against the metric's own history'. However, it does not explicitly state when not to use it or mention alternatives among siblings, slightly limiting guidance.

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