platform_baseline

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

With no annotations, the description must carry behavioral disclosure. It clearly states that the tool aggregates recent anomalous detections and provides interpretive guidance. However, it does not mention whether the operation is read-only, how results are returned, or how the window/by parameters affect behavior—gaps that are more significant given the absence of annotation support.

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 compact, front-loaded with the core question, and immediately states the action and the critical usage instruction. Every sentence earns its place, and there is no filler or redundant information.

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 simple two-optional-parameter tool with no annotations and no output schema, the description gives enough context for an agent to know when to call it and what it conceptually does. But it leaves parameter semantics and expected return shape undocumented, which creates a moderate gap for correct invocation and result interpretation.

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 0%, so the description must compensate for explaining the 'by' and 'window' parameters. It does not. The word 'recent' hints at the window, but there is no explanation of the enum values, defaults, or how grouping by type/severity changes the output. The schema provides titles and defaults, but the description adds little parameter-level meaning.

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 clear verb 'aggregates' and identifies the resource: recent anomalous detections, platform-wide. It also states the intended purpose—distinguishing normal volume from real events—and implicitly differentiates from more specific sibling tools by emphasizing platform-wide aggregation. However, it does not explicitly name a sibling or contrast itself with tools like detections or identify.

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 strong usage guidance: 'Call this BEFORE describing anything as anomalous.' It also conveys that this is the right tool for platform-wide anomaly context and warns against misinterpreting spikes. It does not explicitly state when not to use it or name alternatives, but the usage context is clear.

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