OpenAI MCP Server

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:

npx -y @smithery/cli install @thadius83/mcp-server-openai --client claude

Manual Installation

1. Clone the Repository:

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

{
  "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
   • 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:

<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:

{
  "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

# 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