analytics_drop_attribution

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

No annotations are provided, so the description carries the full burden. It mentions the analytical scope (device causes, algorithm updates) but does not disclose behavioral traits such as whether the tool is read-only, what data sources it accesses, the nature of the analysis (statistical, API-based), or how results are returned. The brevity leaves significant gaps in understanding the tool's behavior.

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, front-loaded sentence that efficiently conveys the tool's purpose without any filler. Every word earns its place, and the structure is optimal for quick understanding.

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 an analytical tool with no output schema and without annotations, the description is insufficiently complete. It fails to explain the result format, any prerequisites (e.g., analytics setup), or how the analysis processes inputs. Given the complexity of traffic drop analysis, an agent would need more context to invoke the tool correctly.

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% with clear parameter descriptions (siteUrl as URL, days as lookback with default, threshold as standard deviation sensitivity). The description adds no extra meaning beyond the schema—it does not relate the parameters to the device/algorithm attribution goal. Baseline 3 is appropriate since the schema already documents parameters well.

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: analyzing a significant traffic drop to attribute it to specific devices (mobile/desktop) or coincide with known Google algorithm updates. It uses a specific verb 'Analyze' and noun 'traffic drop', and the detail about devices and algorithm updates distinguishes it from sibling tools like analytics_anomalies (generic anomaly detection) or analytics_traffic_sources (source-level analysis).

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 usage when investigating a significant traffic drop, but it does not provide explicit guidance on when to use this tool versus alternatives like analytics_anomalies or bing_analytics_drop_attribution. No when-not-to-use conditions or prerequisites are mentioned, leaving the agent to infer usage context.

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