OpenAI WebSearch MCP Server

This MCP server provides access to OpenAI's websearch functionality through the Model Context Protocol. It allows AI assistants to search the web during conversations with users, providing up-to-date information that may not be available in the assistant's training data. The server can be installed and configured for use with Claude.app or Zed editor.

One click installation & Configuration# Claude

!!Can using this command auto update configure file（Recommend）

OPENAI_API_KEY=sk-xxxx uv run --with uv --with openai-websearch-mcp openai-websearch-mcp-install

sk-xxxx is your API key. You can get it from openai's open platform

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Available Tools

• web_search - Call openai websearch as tool.
• Required arguments:
• type (string): web_search_preview
• search_context_size (string): High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.
• user_location (object or null)
• type (string): The type of location > approximation. Always approximate.
• city (string): Free text input for the city of the user, e.g. San Francisco.
• country (string): The two-letter ISO country code of the user, e.g. US.
• region (string): Free text input for the region of the user, e.g. California.
• timezone (string): The IANA timezone of the user, e.g. America/Los_Angeles.

Manual installation and configuration

Please make sure uvx is installed before installation

Add to your Claude settings:

1、Using uvx

"mcpServers": {
  "openai-websearch-mcp": {
    "command": "uvx",
    "args": ["openai-websearch-mcp"],
    "env": {
        "OPENAI_API_KEY": "your-api-key-here"
    }
  }
}

2、Using pip installation

1）install openai-websearch-mcp via pip:

pip install openai-websearch-mcp

2）modify your Claude settings

"mcpServers": {
  "openai-websearch-mcp": {
    "command": "python",
    "args": ["-m", "openai_websearch_mcp"],
    "env": {
        "OPENAI_API_KEY": "your-api-key-here"
    }
  }
}

Configure for Zed

Add to your Zed settings.json:

Using uvx

"context_servers": [
  "openai-websearch-mcp": {
    "command": "uvx",
    "args": ["openai-websearch-mcp"],
    "env": {
        "OPENAI_API_KEY": "your-api-key-here"
    }
  }
],

Using pip installation

"context_servers": {
  "openai-websearch-mcp": {
    "command": "python",
    "args": ["-m", "openai_websearch_mcp"],
    "env": {
        "OPENAI_API_KEY": "your-api-key-here"
    }
  }
},

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx openai-websearch-mcp