What is Gpt 4o Search Mcp

gpt-4o-search-mcp is a Model Context Protocol (MCP) server that provides access to OpenAI’s gpt-4o-search-preview model over MCP, enabling users to perform search operations efficiently.

Use cases

Use cases for gpt-4o-search-mcp include integrating search functionality into applications, enhancing data retrieval processes, and providing intelligent search solutions in customer service or content management systems.

How to use

To use gpt-4o-search-mcp, set up environment variables by copying ‘.env.example’ to ‘.env’, then either deploy using Docker by building the image and running the container, or deploy using Python by creating a virtual environment and running the application with the necessary dependencies.

Key features

Key features of gpt-4o-search-mcp include easy deployment via Docker, flexible environment configuration, and the ability to perform search operations using a simple HTTP interface.

Where to use

gpt-4o-search-mcp can be used in various fields such as web applications, data analysis, and any domain requiring efficient search capabilities within a model context.

Project Overview

A Model Context Protocol (MCP) server which makes OpenAI’s gpt-4o-search-preview model accessible over MCP.

• app/app.py: Main application entry point.
• requirements.txt: Lists Python dependencies required to run the application.
• dockerfile: Instructions for building and running the application in a Docker container.
• .env.example: Example environment variables file. Copy this to .env and update values as needed.

Deployment Instructions

1. Environment Variables

Before running the application, set up your environment variables:

1. Copy .env.example to .env:

   cp .env.example .env
   

2. Edit .env and update the values as needed for your environment.

2. Deploying with Docker

1. Build the Docker image:

   docker build -t my-python-app -f dockerfile .
   

2. Run the container:

   docker run --env-file .env -p 8000:8000 my-python-app
   

3. Deploying with Python (virtualenv)

1. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set environment variables (see .env.example).
4. Run the application:

   python app/app.py
   

Example: Using roo code to connect to MCP

Below is an example configuration block for the gpt-4o-search MCP service:

Python Example: Performing a “search” Operation

The following Python code demonstrates how to use the above configuration to connect to the MCP service and perform a “search” operation using roo code principles. This example uses the requests library to send a search request to the MCP endpoint.

from mcp import MCPClient

# Initialize the MCP client for the gpt-4o-search server
client = MCPClient("http://link-to-where-service-is-hosted:8000/sse")

# Perform a "search" operation
result = client.tool("search", {"query": "What is Model Context Protocol?"})

print("Search result:", result)
### Explanation

- **MCPClient**: The official `mcp` Python library provides the `MCPClient` class to connect to an MCP server.
- **client = MCPClient(...)**: Initializes the client with the URL of the gpt-4o-search MCP server.
- **client.tool("search", {...})**: Performs the "search" operation by specifying the tool name and parameters as a dictionary.
- **Result**: The result of the search operation is printed.

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# Notes

- Only perform the work outlined above and do not deviate from these instructions.
- For further details, refer to the individual files and comments within the codebase.