OpenAI MCP Server

MCP server providing comprehensive access to OpenAI's API capabilities.

Features

Core Capabilities

• Chat Completions - Generate responses using GPT-4o, GPT-4, and GPT-3.5 models
• Embeddings - Create vector embeddings for semantic search and similarity
• Image Generation - Generate images with DALL-E 3 and DALL-E 2
• Text-to-Speech - Convert text to natural-sounding speech
• Speech-to-Text - Transcribe audio using Whisper
• Vision Analysis - Analyze images with GPT-4 Vision
• Content Moderation - Check content against usage policies
• Model Management - List and explore available models

Installation

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

1. Set up your environment variables:

cp .env.example .env
# Edit .env and add your OpenAI API key

Configuration

Get Your OpenAI API Key

1. Go to https://platform.openai.com/api-keys
2. Sign in or create an account
3. Click "Create new secret key"
4. Copy the key and add it to your .env file

Running the Server

HTTP Mode (Recommended for NimbleBrain)

Start the server:

# Set your API key
export OPENAI_API_KEY=your_api_key_here

# Run the server (default port 8000)
fastmcp run openai_server.py

# Or specify a custom port
fastmcp run openai_server.py --port 8080

The server will be available at http://localhost:8000

Claude Desktop Configuration

Add to your claude_desktop_config.json:

HTTP Configuration:

{
  "mcpServers": {
    "openai": {
      "url": "http://localhost:8000"
    }
  }
}

Alternative - Direct Python (stdio):

If you need stdio mode instead of HTTP, you can run directly:

Windows (%APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "openai": {
      "command": "python",
      "args": ["-m", "fastmcp", "run", "openai_server.py"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

macOS (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "openai": {
      "command": "python3",
      "args": ["-m", "fastmcp", "run", "openai_server.py"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Available Tools

chat_completion

Generate conversational responses using OpenAI's chat models.

Parameters:

• messages (required): List of message objects with 'role' and 'content'
• model: Model name (default: "gpt-4o-mini")
• temperature: Creativity level 0-2 (default: 1.0)
• max_tokens: Maximum response length
• response_format: Optional "json_object" for JSON responses

Example:

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain quantum computing in simple terms."}
]

create_embedding

Generate vector embeddings for text.

Parameters:

• text (required): Text to embed
• model: Embedding model (default: "text-embedding-3-small")

Use Cases:

• Semantic search
• Document similarity
• Clustering and classification
• Recommendation systems

generate_image

Create images from text descriptions using DALL-E.

Parameters:

• prompt (required): Description of desired image
• model: "dall-e-3" or "dall-e-2" (default: "dall-e-3")
• size: Image dimensions (1024x1024, 1792x1024, 1024x1792)
• quality: "standard" or "hd" (DALL-E 3 only)
• n: Number of images (1-10, only 1 for DALL-E 3)

text_to_speech

Convert text to natural-sounding audio.

Parameters:

• text (required): Text to convert
• voice: alloy, echo, fable, onyx, nova, shimmer (default: "alloy")
• model: "tts-1" or "tts-1-hd" (default: "tts-1")
• speed: Speech rate 0.25-4.0 (default: 1.0)

Returns: Base64 encoded MP3 audio

transcribe_audio

Transcribe audio to text using Whisper.

Parameters:

• audio_file_base64 (required): Base64 encoded audio file
• model: "whisper-1"
• language: Optional language code (auto-detected if not provided)
• response_format: json, text, srt, vtt, verbose_json

analyze_image

Analyze images using GPT-4 Vision.

Parameters:

• image_url (required): URL of image to analyze
• prompt: Question about the image (default: "What's in this image?")
• model: Vision model (default: "gpt-4o-mini")
• max_tokens: Maximum response length

moderate_content

Check if content violates OpenAI's usage policies.

Parameters:

• text (required): Content to moderate
• model: "text-moderation-latest" or "text-moderation-stable"

Returns: Flags and scores for various content categories

list_models

Get all available OpenAI models with metadata.

Usage Examples

Chat Conversation

{
  "messages": [
    {"role": "system", "content": "You are a creative writing assistant."},
    {"role": "user", "content": "Write a haiku about programming."}
  ],
  "model": "gpt-4o",
  "temperature": 0.8
}

Generate Marketing Image

{
  "prompt": "A modern minimalist logo for a tech startup, blue and white color scheme, professional",
  "model": "dall-e-3",
  "size": "1024x1024",
  "quality": "hd"
}

Create Product Description Embeddings

{
  "text": "Wireless Bluetooth headphones with active noise cancellation and 30-hour battery life",
  "model": "text-embedding-3-small"
}

Transcribe Meeting Recording

{
  "audio_file_base64": "<base64_encoded_audio>",
  "language": "en",
  "response_format": "verbose_json"
}

Model Recommendations

Chat Models

• gpt-4o: Best overall, multimodal, fast
• gpt-4o-mini: Cost-effective, very fast
• gpt-4-turbo: High intelligence, good for complex tasks
• gpt-3.5-turbo: Fast and affordable for simple tasks

Embedding Models

• text-embedding-3-small: Best price/performance (1536 dimensions)
• text-embedding-3-large: Highest quality (3072 dimensions)

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