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openai_websearch

by hffmnnj

Web Search

web_search

Search the web with OpenAI's server-side search for current, cited results with real URLs. Get clean answers using your ChatGPT subscription.

Instructions

Search the web using OpenAI's native server-side web search. Returns clean, up-to-date results with real URLs and citations. Powered by your ChatGPT/Codex subscription.

  • contextSize: "low" (fast/cheap), "medium" (balanced, default), "high" (thorough/deep)

  • All search happens server-side at OpenAI, not locally

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOpenAI model to use (default: gpt-5.6-luna)
queryYesWhat to search for. Be specific for best results.
context_sizeNoHow much web context to retrievemedium
Behavior4/5

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

With no annotations, the description carries the transparency burden and does well by disclosing that search is server-side, powered by a subscription, and that context_size affects speed/quality. It also promises real URLs and citations, giving useful behavioral expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two short sentences plus a bullet for context_size, with the core purpose front-loaded. Every sentence adds unique value without redundancy or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description explains key return characteristics (clean results, real URLs, citations). It covers the essential behavior and parameter variants for a simple search tool, though it omits failure modes or edge cases like empty results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing a baseline of 3. The description adds extra meaning to context_size by mapping values to cost/speed trade-offs ('fast/cheap' vs 'thorough/deep'), going beyond the schema's basic 'How much web context to retrieve'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Search the web' and names the resource as the web, clearly indicating the tool's function. It also mentions returning URLs and citations, which helps distinguish it from the sibling tool 'image_search', though it does not explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context about server-side execution and subscription reliance, plus context_size options, but it does not explicitly say when to use this tool over image_search or when not to use it. Usage is implied rather than explicitly guided.

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

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