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

With no annotations, the description carries the burden of behavioral disclosure and does well: it lists return fields in detail, specifies that channelAttribution sums to 1, and handles the null-return edge case. However, it mentions supported models without indicating how a model is selected given only campaign_id in the schema, leaving a minor ambiguity.

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-organized with clear headings (WHEN TO USE, RETURNS) and bulleted lists. It is front-loaded with a clear summary sentence and every line provides meaningful information without redundancy.

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 no output schema and no annotations, the description is remarkably complete: it explains what the tool does, when to use it, what the return object contains, and a key edge case (null response). It is more than sufficient for an agent 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?

The schema already provides 100% coverage with campaign_id described as 'Campaign identifier'. The description adds no further parameter-level detail, so the baseline of 3 applies.

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 'Get multi-touch attribution model results for a campaign' with a specific verb and resource, and lists supported models (time_decay, position_based, attention_weighted) which distinguishes it from sibling attribution tools like get_attribution_timeseries or get_campaign_attribution.

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 context (understanding DOOH fit, credit allocation, quantifying DOOH contribution) but does not mention exclusions or explicitly name alternative tools for different scenarios, so it stops short of a 5.

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