patent_search

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds genuinely useful behavioral context beyond the annotations: each result includes bibliographic details, zero-result/error responses return structured JSON with recovery hints, and results are cached for 24 hours. This enriches the agent's mental model without contradicting any annotation.

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 dense but every sentence earns its place: purpose, query formats, result contents, sibling routing, error behavior, and freshness. It is slightly lengthy, but for an 11-parameter tool with a wide search scope, the detail is justified and well front-loaded with the core purpose before alternatives.

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's complexity (11 parameters, 3 enums, broad query semantics) and the presence of an output schema, the description covers all essential context an agent needs: when to use it, how to phrase queries, what results look like, how errors are handled, and how fresh the data is. Nothing critical is missing for correct invocation.

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%, so the schema already documents every parameter. The description goes beyond the schema by providing example query forms ('US11234567' or 'machine learning video encoding'), explaining that company name variations are auto-matched, and clarifying that query is optional when assignee or inventor is supplied. This adds useful semantics for the primary query parameter.

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 names a specific verb and resource ('Search patents') and enumerates distinct purposes ('prior art, competitive landscape mapping, or to look up a specific patent'). It also explicitly differentiates from siblings by pointing to academic_search for research papers and web_search for general technical content, so an agent can reliably tell it apart without opening schemas.

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 provides explicit when-to-use guidance ('Reach for this when the question is about inventions or IP') and names alternatives with their conditions ('use academic_search for research papers or web_search for general technical content'). It also gives concrete query examples and notes automatic assignee variation matching, which helps decide how to formulate a search.

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