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

Annotations already declare the tool read-only, idempotent, and open-world. The description adds meaningful context beyond those: it states the search matches title/brand/model, returns the canonical OpenAI shape with only id/title/url, and directs the agent to call fetch() for full documents. This helps set expectations about the lightweight nature of the results and the follow-up workflow, though it does not cover rate limits or pagination.

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 only three sentences, front-loaded with the tool's compatibility purpose, then the core search behavior, then the output format and next step. Every sentence contributes value, and there is no redundant or filler wording. It is concise yet information-dense.

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 simplicity (one parameter) and the presence of annotations and output schema, the description is fully complete. It clarifies the scope (active UK used-bike listings), search fields, output shape, and the follow-up call to fetch. No critical information is missing for an agent to effectively select and invoke this 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?

The schema describes 'query' only as a free-text string. The description adds crucial semantics by explaining that the query matches title, brand, and model fields, and that it returns results in a specific shape. This gives the agent a better understanding of how the query is interpreted, going beyond the bare schema description.

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 specifies exactly what the tool does: search Cyclesite's active UK used-bike listings by free-text query. It names the resource (active UK used-bike listings), the action (search), and the searchable fields (title, brand, model). It also distinguishes itself from siblings by highlighting 'OpenAI deep-research / company-knowledge compatibility' and the canonical OpenAI output shape, clearly differentiating it from alternative search tools like search_bikes.

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 gives clear context that this tool is intended for OpenAI deep-research / company-knowledge use cases, and it explicitly tells the agent to use the returned id to call fetch() for the full document. This is actionable guidance, though it does not explicitly mention when not to use other search siblings or provide exclusions. Overall, the intended usage is clear.

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