gpt-4o-search-mcp

# Project Overview A Model Context Protocol (MCP) server which makes OpenAI's gpt-4o-search-preview model accessible over MCP. - [`app/app.py`](app/app.py): Main application entry point. - [`requirements.txt`](requirements.txt): Lists Python dependencies required to run the application. - [`dockerfile`](dockerfile): Instructions for building and running the application in a Docker container. - [`.env.example`](.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`: ```bash 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: ```bash docker build -t my-python-app -f dockerfile . ``` 2. Run the container: ```bash docker run --env-file .env -p 8000:8000 my-python-app ``` ## 3. Deploying with Python (virtualenv) 1. Create and activate a virtual environment: ```bash python3 -m venv venv source venv/bin/activate ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Set environment variables (see `.env.example`). 4. Run the application: ```bash 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: ```json "gpt-4o-search": { "url": "http://link-to-where-service-is-hosted:8000/sse", "transport": "http", "alwaysAllow": [ "search" ], "timeout": 300 } ``` ## 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. ```python 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. --- # 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.