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

The description adds behavior beyond annotations: it reveals parallel execution of multiple AI models and the return of ranked results with Hit Scores. It also uses 'potentially infringe' to set probabilistic expectations. Annotations provide openWorldHint and non-read-only hints, but the description enriches the agent's understanding of how the tool operates, though it does not mention cost/credit consumption.

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?

Three concise, front-loaded sentences: purpose first, then behavioral detail, then usage guidance. No filler or redundancy. Every sentence adds value, making it easy for an agent to parse quickly.

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 4-parameter tool with no output schema, the description covers purpose, behavior, and usage context. It mentions the return format ('ranked results with Hit Scores') which is a minimal but acceptable descriptor. However, it lacks details on pagination or how results might be retrieved later (sibling get_result hints at persistence), leaving some gaps for a complex tool.

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% with detailed parameter descriptions (examples, defaults, enums). The description adds no extra parameter semantics beyond confirming products/standards which aligns with the 'mode' parameter. Baseline 3 is appropriate because the schema carries the full semantic load.

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 uses a specific verb ('Search'), identifies the resource ('a given patent'), and defines the scope ('products or technical standards') with clear intent ('potentially infringe'). It differentiates from siblings by mentioning parallel AI models and Hit Scores, and the usage cases (find infringers, check infringement, screen a patent) distinguish it from claimhit_get_result or claimhit_rerun_search.

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?

Explicitly states when to use this tool: 'when asked to find infringers, check infringement, or screen a patent.' This is clear context, but it does not mention alternatives or exclusions, failing to reach the level of explicitly naming sibling tools for different scenarios.

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