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

No annotations are present, so the description must carry the full behavioral burden. It implies a read-only operation by saying 'list', and it adds useful context by explaining the comparison baseline ('versus the prior mining job'). However, it does not disclose ordering, empty-return behavior, or authorization requirements, leaving modest gaps in behavioral transparency.

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 with no filler. The first sentence front-loads the verb, resource, and definition; the second gives concrete usage triggers. 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?

For a list tool with two optional, well-documented parameters and no output schema, the description provides the core facts: what is listed, what qualifies as drift, and when to call it. A minor gap is the absence of a note about the alert object's shape, but sibling tools like explain_drift suggest that detail is available elsewhere.

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?

The input schema already documents both parameters (limit and severity) with defaults, ranges, and enums, giving 100% schema description coverage. The tool description adds no additional parameter-level meaning beyond that, so the baseline of 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?

The description states a specific verb ('list') and a precise resource ('active drift alerts'), and then defines what those alerts are: recommendation rules whose confidence has materially changed versus the prior mining job. This clearly distinguishes it from siblings like get_forecast_alerts and get_opportunities by naming the exact object type and comparison baseline.

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 gives explicit use-case triggers ('what's changed?', 'is my model still accurate?', 'are any rules drifting?', SKU swap / seasonal shift). It does not name sibling alternatives or state when not to use them, but the trigger phrases make the intended invocation context clear.

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