analytics_drop_attribution

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions analysis of device/algorithm causes, but fails to explain whether the tool modifies data, requires specific permissions, or provides any output details. This is insufficient for an agent to understand non-functional aspects.

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 sentence of about 20 words, front-loaded with the key action and scope. Every word earns its place, with no redundancy or filler.

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?

Given the lack of output schema and annotations, the description is somewhat complete for a straightforward analysis tool. However, it omits details on return format, prerequisites (e.g., date range, data availability), and interpretation of results, leaving gaps for agent decision-making.

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 100%, with each parameter (days, siteUrl, threshold) having a clear description. The tool description does not add further meaning beyond summarizing the purpose. Baseline 3 is appropriate as the schema does the heavy lifting.

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 identify device-specific causes or correlation with Google algorithm updates. It uses a specific verb ('analyze') and resource ('traffic drop'), effectively distinguishing it from sibling tools like analytics_trends or analytics_anomalies.

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 when to use (when there's a significant traffic drop and need to identify cause), but it does not explicitly state when not to use or provide alternatives. While context is clear, the lack of exclusions or comparison to other tools limits guidance.

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