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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the editorial data source, the specific comparison dimensions, and the output (winner determination with source-linked URLs). It also provides a methodology link, offering transparency on how the score is calculated. This goes beyond the annotations, though it doesn't disclose potential limitations like data recency.

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 three sentences, front-loaded with the core purpose. Every sentence adds value: what it does, what it compares, and what it returns. There is no fluff or redundant repetition of annotations.

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 one parameter and no output schema, the description fully explains the output ('data-backed winner determination with source-linked review page URLs') and the data source. It also provides a methodology link. The behavior is sufficiently complete for an agent to invoke and interpret results.

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 coverage is 100%: the only parameter 'products' is documented with a clear description and example. The tool description reinforces the 2-4 product limit but doesn't add new semantic detail beyond what the schema already provides. Baseline score 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 performs 'Side-by-side comparison of 2-4 smart home products' with specific data dimensions (SHE Consensus Score, ecosystem support, subscription costs). It uses a specific verb and resource, and the comparison scope distinguishes it from sibling tools like check_compatibility or get_product_verdict.

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 implies the use case: comparing multiple smart home products side-by-side. It provides clear context about what data is compared and the outcome. However, it does not explicitly mention alternatives or when not to use this tool (e.g., for a single product verdict), so it falls 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.