Get Campaign Marginal Returns

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

Beyond the annotations (readOnly/idempotent/openWorld), the description discloses substantive behavior: how campaign shapes are derived (rescaled by spend share and relative efficiency), that daily spend is observed spend over inclusive calendar days rather than a configured budget, that missing currency/curve/fact coverage yields unavailable recommendations, and that absent posterior evidence means no interval rather than zero uncertainty. It also confirms no fit or platform change is triggered.

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?

Purpose is front-loaded, then Args, then Returns, so structure is strong. It is somewhat long and the 'no fit, posterior job or platform change is started' clause partially overlaps the readOnly annotation, but most sentences carry genuine, non-redundant information.

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 analytical tool with four params and an output schema, the definition is complete: prerequisites, input semantics, caveats on what the numbers do and do not mean, and failure behavior are all covered, and it even summarizes the return shape despite an output schema existing.

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?

With 0% schema description coverage, the description carries the full burden and delivers: model_hash must be an owned, completed MMM with compatible provenance; start/end are inclusive ISO dates (YYYY-MM-DD); level is campaign or adset with ad sets inheriting the campaign's channel. All four parameters get meaning the bare schema lacks.

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 opening sentence names a specific verb and resource (read channel-derived marginal returns for campaigns or ad sets) and delimits scope against adjacent concepts by stating these are 'not independently measured campaign saturation curves or causal campaign effects.' It does not name a sibling tool directly, so it falls just short of a 5.

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?

It states a precondition ('Requires read:models') and constrains model_hash to an 'Owned, completed MMM with compatible curve provenance and campaign facts,' plus a 'do not substitute defaults' rule on missing data. However, it never says when to prefer this over neighbors like get_campaign_report, get_campaign_incrementality, or recommend_campaign_budgets, so routing guidance is only implied.

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