OpenAI Web Search MCP Server: Multi-Config, Dev Agility

Maximize OpenAI web search performance with MCP Server’s flexible multi-config protocols, delivering scalable, enterprise-grade solutions for seamless data integration and developer agility.

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About OpenAI Web Search MCP Server

What is OpenAI Web Search MCP Server: Multi-Config, Dev Agility?

This TypeScript-based middleware solution bridges AI models with real-time web search capabilities via OpenAI's Responses API. By leveraging the web_search_preview feature, it empowers developers to integrate dynamic internet data retrieval into MCP-driven applications through standardized protocol interfaces. The architecture emphasizes flexible configuration management and rapid iteration cycles, addressing evolving development needs without compromising operational stability.

How to use OpenAI Web Search MCP Server: Multi-Config, Dev Agility?

Initialization begins with either transient execution via npx or persistent installation through npm. Configuration adapts to specific environments, requiring API key authentication and optional port customization. Integration with MCP clients like Claude Desktop necessitates precise server registration in application manifests, ensuring secure credential handling and protocol compliance through environment variable injection.

OpenAI Web Search MCP Server Features

Key Features of OpenAI Web Search MCP Server: Multi-Config, Dev Agility?

Central to this offering is the adaptive configuration system enabling multi-environment deployments. The server's modular design supports concurrent protocol versions while maintaining backward compatibility. Real-time search capabilities are delivered through optimized API orchestration, minimizing latency in result delivery. A standout feature is the portability of development workflows, allowing seamless transitions from prototyping to production without architectural retooling.

Use cases of OpenAI Web Search MCP Server: Multi-Config, Dev Agility?

OpenAI Web Search MCP Server FAQ

FAQ from OpenAI Web Search MCP Server: Multi-Config, Dev Agility?

Q: Is environment variable setup mandatory?
A: Secure credential management requires explicit API key declaration through OPENAI_API_KEY.
Q: Can I override default ports programmatically?
A: Port assignments are dynamically controlled using process.env.PORT in runtime contexts.
Q: How does development mode differ from production?
A: The npm run dev command enables hot-reloading and verbose logging, contrasting with optimized production builds.
Q: What guarantees protocol compatibility?
A: Rigorous testing against MCP v2.0 specifications ensures seamless interaction with certified clients.

Content

OpenAI Web Search MCP Server

A TypeScript implementation of an MCP server that provides web search functionality using OpenAI's web search preview feature. This server utilizes OpenAI's latest Responses API with the web_search_preview capability, allowing AI models to perform real-time web searches through the OpenAI API.

Installation

You can run this package directly using npx:

npx openai-websearch-mcp-server

Or install it globally:

npm install -g openai-websearch-mcp-server

Usage with MCP Clients

This server is designed to be used with MCP (Model Context Protocol) clients. Here's how to set it up with different clients:

Claude Desktop

Add the following configuration to your Claude Desktop settings:

{
  "mcpServers": {
    "openai_websearch": {
      "command": "npx",
      "args": [
        "-y",
        "openai-websearch-mcp-server"
      ],
      "env": {
        "OPENAI_API_KEY": "your_api_key"
      }
    }
  }
}

Replace your_api_key with your actual OpenAI API key.

Environment Setup

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY='your-api-key-here'

Run the server:

openai-websearch-mcp

By default, the server runs on port 3000. You can change this by setting the PORT environment variable:

PORT=8080 openai-websearch-mcp

API

The server provides a web search tool that can be used through the MCP protocol. The tool is named web_search and accepts a query string as input. Under the hood, it uses OpenAI's Responses API with the web_search_preview feature to perform real-time web searches, providing up-to-date information from across the internet.

Development

To set up the development environment:

Clone the repository
Install dependencies:

npm install
Start the development server:

npm run dev

Building

To build the package:

npm run build

License

MIT

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