mcp-openai-websearch 0.1.2

using openai websearch as mcp server

OpenAI WebSearch MCP Servr

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.

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.

Installation

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run openai-websearch-mcp.

Using PIP

Alternatively you can install openai-websearch-mcp via pip:

pip install openai-websearch-mcp

After installation, you can run it as a script using:

python -m openai-websearch-mcp

Configuration

Configure for Claude.app

Add to your Claude settings:

Using uvx

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

Using pip installation

"mcpServers": {
  "openai-websearch-mcp": {
    "command": "python",
    "args": ["-m", "mcp_openai_websearch"],
    "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", "mcp_openai_websearch"],
    "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