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

Annotations already declare this a read-only, idempotent, closed-world operation, so safety is covered. The description adds useful scope detail (property-level AND global events, category filtering via "algo_update") but gives no return shape and asserts a default window that conflicts with the schema, which undercuts trust in the behavioral claims.

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?

One tight paragraph that front-loads the resource and event types, then the usage framing, then the default window. The trailing Portuguese echo is mild redundancy but consistent with the tool's bilingual convention and does not bury the substance.

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 query with three optional, fully schema-documented parameters and no output schema, the description supplies what the schema cannot: what an annotation actually is and why you would fetch it. Its one completeness defect is the incorrect default window, which should be corrected rather than the description being expanded.

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 the schema already documents all four parameters thoroughly (including the 28-day default). The only parameter-relevant sentence in the description, "Default: last 90 days," directly contradicts the schema's "Window length in days (default 28)," which risks an agent mis-sizing its query rather than clarifying it.

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 specific verb+resource (retrieve events annotated in a period) and enumerates the event kinds returned: deploys, content changes, manual notes, and Google algorithm updates with the category token "algo_update". No sibling tool deals with annotations, so an agent can distinguish it from detect_anomalies or detect_change_points immediately.

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?

"Use to correlate drops or gains with causes" gives an explicit analytic context that tells the agent when this tool is the right choice rather than a metrics tool. It stops short of naming an exclusion or a paired alternative, which keeps it out of 5 territory.

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