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

Annotations already provide readOnlyHint=true, openWorldHint=true, and idempotentHint=true. The description adds that the tool returns structured calibration metrics and attribution adjustments, which is consistent. It does not contradict annotations and provides minor behavioral context beyond the schema, but does not elaborate on side effects or performance characteristics.

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 four sentences long, starting with the persona and purpose, then inputs and outputs, then benefit. It is structured and clear, though could be slightly more concise by removing filler phrases like 'Useful for...'.

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?

The tool has 6 parameters (3 required) and an output schema (not shown). The description covers the main inputs and outputs, and mentions the benefit. It does not mention async behavior (covered by parameter description) or the confidenceThreshold parameter, but these are present in the schema. Overall, it provides sufficient context 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?

Input schema has 100% description coverage, and the description adds context by mentioning 'model parameters, date ranges, and impression IDs' and specifically referencing OpenRTB and FreeWheel data sources. This enriches understanding of the data context, but the description does not detail every parameter (e.g., confidenceThreshold is not mentioned). Baseline of 3 is appropriate given schema coverage.

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 it calibrates marketing mix models (MMM) using OpenRTB impression-level data from specific sources like FreeWheel Marketplace. It distinguishes itself from siblings (e.g., programmatic_brand_safety_auditor) by targeting the ad_revenue_ops persona and focusing on attribution calibration.

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 defines the target persona (ad_revenue_ops) and the use case (calibrating MMM with programmatic data). It does not explicitly state when to avoid using it or compare with alternatives, but the context sufficiently guides appropriate usage.

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