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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention that this is a read-only operation, potential permissions required, rate limits, or edge cases like missing campaigns. The 'RETURNS' section describes output format but not behavioral traits, leaving a transparency gap.

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 well-structured with a clear main sentence, 'WHEN TO USE' bullets, 'RETURNS' field list, and a practical example. It is concise, front-loaded, and every section adds value without unnecessary fluff.

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?

Since there is no output schema, the description compensates by listing all return fields (date, uniqueDevices, liftPct, etc.) and providing an example. It covers the essential context for a daily timeseries tool, though it omits minor details like default date ranges (already in schema) or significance interpretation. Overall, it is adequately complete for this complexity.

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%, so the parameters (campaign_id, start_date, end_date) are already well-documented. The description adds a concrete example showing parameter usage, but it does not add meaning beyond the schema, such as default behaviors or constraints. This meets the baseline but does not exceed 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?

The description opens with 'Get daily attribution timeseries for a campaign,' which clearly states the verb (get), resource (attribution timeseries), and scope (daily, for a campaign). This is specific enough to distinguish from sibling tools like get_campaign_attribution or get_multi_touch_attribution, and the 'RETURNS' section reinforces the exact resource.

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 'WHEN TO USE' section provides clear use cases: tracking attribution trends over time, identifying strongest lift days, and building dashboards with daily granularity. However, it does not explicitly mention when not to use this tool or name alternative tools, so it falls short of the full 'when/when-not/alternatives' criterion.

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