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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, covering safety and side effects. The description adds context about data sources (FreeWheel Marketplace) and output type (calibration metrics and attribution adjustments). However, it does not elaborate on behavior such as processing semantics, error conditions, or how calibration metrics are computed, leaving some behavioral details undisclosed.

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 composed of three sentences and is reasonably concise. It opens with the target persona and primary function, then lists inputs/outputs, and ends with use cases. While slightly redundant in listing inputs already covered by schema, it remains compact and well-structured.

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 tool has six parameters and an output schema, the description provides sufficient context about the data source, target persona, and intended use. It does not explain all details like the async parameter or confidence threshold, but the schema covers those. The description is complete enough for an agent to select and 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 description coverage is 100%, so the schema already documents all parameters (modelId, dates, impressionIds, confidenceThreshold, async). The description adds little beyond the schema, only generically mentioning 'model parameters, date ranges, and impression IDs', which does not enhance understanding of individual parameters. Baseline of 3 is appropriate.

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 'calibrates marketing mix models (MMM)' using OpenRTB impression-level data, specifying a distinct verb and resource. It also names the persona (ad_revenue_ops) and differentiates from siblings by focusing on MMM calibration with programmatic data, unlike adjacent tools like programmatic_brand_safety_auditor.

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?

Provides clear context on when to use the tool: for ad revenue operations, improving model accuracy with real-time bidding data, and validating revenue attribution across programmatic channels. It does not explicitly mention when not to use it or name alternatives, but the use cases are specific enough to guide selection.

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