OpenAI WebSearch MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key from the OpenAI Platform | |
| OPENAI_DEFAULT_MODEL | No | Default model to use | gpt-5-mini |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| openai_web_searchA | OpenAI Web Search with reasoning models. For quick multi-round searches: Use 'gpt-5-mini' with reasoning_effort='low' for fast iterations. For deep research: Use 'gpt-5' with reasoning_effort='medium' or 'high'. The result is already multi-round reasoned, so agents don't need continuous iterations. Supports: gpt-4o (no reasoning), gpt-5/gpt-5-mini/gpt-5-nano, o3/o4-mini (with reasoning). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose of performing web searches with OpenAI models.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'openai_web_search' follows a clear and descriptive pattern that would be consistent if more tools existed.
A single tool is generally too few for a server's purpose, as it limits functionality and flexibility. While the tool is well-described, the server's scope appears to be web search, which could benefit from additional tools for filtering, refining, or managing searches.
The server is severely incomplete for web search functionality. It lacks essential operations such as filtering results, handling pagination, saving or retrieving search history, or configuring search parameters beyond model selection. This will likely cause agent failures in complex search tasks.