openai_web_search

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

With no annotations provided, the description carries the full burden. It discloses that the result is 'already multi-round reasoned, so agents don't need continuous iterations', providing useful behavioral insight about the reasoning process. However, it doesn't mention potential rate limits, API requirements, or other operational characteristics. Since web search is inherently read-only, that aspect is implied.

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 compact and front-loaded, with the main purpose stated in the first line. It then provides two clear usage scenarios and a list of supported models. Every sentence earns its place, with no redundant or filler content.

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 that the tool has a rich schema and an output schema (which explains return values), the description doesn't need to cover those. It adequately covers the critical model selection guidance and reasoning effort recommendations, which are the non-obvious parts of using this tool. It's complete enough for an agent to decide when and how to invoke it.

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 already includes descriptions for all 6 parameters (100% coverage), so the baseline is 3. The description adds extra meaning by explaining the relationship between models and reasoning_effort for different use cases, which goes beyond the schema's per-parameter descriptions. This helps the agent select appropriate model/effort combinations.

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 'OpenAI Web Search' which indicates the tool performs web searches, and mentions reasoning models. It doesn't use a specific verb like 'search' but the name and description together make the purpose clear. No sibling tools exist to differentiate from, so it doesn't need to distinguish itself.

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 explicit guidance for different usage scenarios: 'For quick multi-round searches: Use gpt-5-mini with reasoning_effort='low'' and 'For deep research: Use gpt-5 with reasoning_effort='medium' or 'high''. This provides clear context on when to use specific model settings, though it doesn't mention when not to use the tool since there are no sibling tools.

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