OpenAI MCP Server

# OpenAI MCP Server [](https://smithery.ai/server/@thadius83/mcp-server-openai) Query OpenAI models directly from Claude using MCP protocol. This fork adds support for o3-mini and gpt-4o-mini models with improved message handling. ## Cline Auto Install ``` Set up the MCP server from https://github.com/thadius83/mcp-server-openai. Use "github.com/thadius83/mcp-server-openai" as the server name in cline_mcp_settings.json. Analyse the readme and instructions below. Do not create new files in the repo, utilise the findings from pyproject.toml, src/mcp_server_openai/server.py, src/mcp_server_openai/llm.py Once installed, demonstrate the server's capabilities by using one of its tools. Installation Steps: # Clone the repository git clone https://github.com/thadius83/mcp-server-openai cd mcp-server-openai # Install the package pip install .` MCP Settings Configuration: The cline_mcp_settings.json should be configured with: Correct server name format: "github.com/thadius83/mcp-server-openai" Python module path structure for the server PYTHONPATH environment variable pointing to the project directory OpenAI API key passed as a command line argument Example configuration: { "mcpServers": { "github.com/thadius83/mcp-server-openai": { "command": "python", "args": [ "-m", "src.mcp_server_openai.server", "--openai-api-key", "your-openai-api-key" ], "env": { "PYTHONPATH": "/path/to/mcp-server-openai" }, "disabled": false, "autoApprove": [] } } } Requirements: Python >= 3.10 OpenAI API key Dependencies installed via pip (mcp>=0.9.1, openai>=1.0.0, click>=8.0.0, pytest-asyncio) Available Tools: Tool Name: ask-openai Description: Ask OpenAI assistant models a direct question Models Available: o3-mini (default) gpt-4o-mini Input Schema: { "query": "Your question here", "model": "o3-mini" // optional, defaults to o3-mini } ``` ## Features - Direct integration with OpenAI's API - Support for multiple models: - o3-mini (default): Optimized for concise responses - gpt-4o-mini: Enhanced model for more detailed responses - Configurable message formatting - Error handling and logging - Simple interface through MCP protocol ## Installation ### Installing via Smithery To install OpenAI MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@thadius83/mcp-server-openai): ```bash npx -y @smithery/cli install @thadius83/mcp-server-openai --client claude ``` ### Manual Installation 1. **Clone the Repository**: ```bash git clone https://github.com/thadius83/mcp-server-openai.git cd mcp-server-openai # Install dependencies pip install -e . ``` 2. **Configure Claude Desktop**: Add this server to your existing MCP settings configuration. Note: Keep any existing MCP servers in the configuration - just add this one alongside them. Location: - macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - Windows: `%APPDATA%/Claude/claude_desktop_config.json` - Linux: Check your home directory (`~/`) for the default MCP settings location ```json { "mcpServers": { // ... keep your existing MCP servers here ... "github.com/thadius83/mcp-server-openai": { "command": "python", "args": ["-m", "src.mcp_server_openai.server", "--openai-api-key", "your-key-here"], "env": { "PYTHONPATH": "/path/to/your/mcp-server-openai" } } } } ``` 3. **Get an OpenAI API Key**: - Visit [OpenAI's website](https://openai.com) - Create an account or log in - Navigate to API settings - Generate a new API key - Add the key to your configuration file as shown above 4. **Restart Claude**: - After updating the configuration, restart Claude for the changes to take effect ## Usage The server provides a single tool `ask-openai` that can be used to query OpenAI models. You can use it directly in Claude with the use_mcp_tool command: ```xml <use_mcp_tool> <server_name>github.com/thadius83/mcp-server-openai</server_name> <tool_name>ask-openai</tool_name> <arguments> { "query": "What are the key features of Python's asyncio library?", "model": "o3-mini" // Optional, defaults to o3-mini } </arguments> </use_mcp_tool> ``` ### Model Comparison 1. o3-mini (default) - Best for: Quick, concise answers - Style: Direct and efficient - Example response: ``` Python's asyncio provides non-blocking, collaborative multitasking. Key features: 1. Event Loop – Schedules and runs asynchronous tasks 2. Coroutines – Functions you can pause and resume 3. Tasks – Run coroutines concurrently 4. Futures – Represent future results 5. Non-blocking I/O – Efficient handling of I/O operations ``` 2. gpt-4o-mini - Best for: More comprehensive explanations - Style: Detailed and thorough - Example response: ``` Python's asyncio library provides a comprehensive framework for asynchronous programming. It includes an event loop for managing tasks, coroutines for writing non-blocking code, tasks for concurrent execution, futures for handling future results, and efficient I/O operations. The library also provides synchronization primitives and high-level APIs for network programming. ``` ### Response Format The tool returns responses in a standardized format: ```json { "content": [ { "type": "text", "text": "Response from the model..." } ] } ``` ## Troubleshooting 1. **Server Not Found**: - Verify the PYTHONPATH in your configuration points to the correct directory - Ensure Python and pip are properly installed - Try running `python -m src.mcp_server_openai.server --openai-api-key your-key-here` directly to check for errors 2. **Authentication Errors**: - Check that your OpenAI API key is valid - Ensure the key is correctly passed in the args array - Verify there are no extra spaces or characters in the key 3. **Model Errors**: - Confirm you're using supported models (o3-mini or gpt-4o-mini) - Check your query isn't empty - Ensure you're not exceeding token limits ## Development ```bash # Install development dependencies pip install -e ".[dev]" # Run tests pytest -v test_openai.py -s ``` ## Changes from Original - Added support for o3-mini and gpt-4o-mini models - Improved message formatting - Removed temperature parameter for better compatibility - Updated documentation with detailed usage examples - Added model comparison and response examples - Enhanced installation instructions - Added troubleshooting guide ## License MIT License