AI API MCP Server

A FastMCP-based Model Context Protocol (MCP) server that provides unified access to multiple AI APIs including OpenAI GPT, Google Gemini, Anthropic Claude, and xAI Grok.

📚 Documentation

• Quick Start - Get started in 5 minutes
• MCP Installation Guide - Setup for Claude Code, Claude Desktop, Cursor, VS Code, and more
• API Reference - Detailed API documentation
• Usage Examples - Practical examples and patterns
• Troubleshooting - Common issues and solutions

Features

• Unified Interface: Single MCP interface for multiple AI providers
• Multiple Providers: Support for OpenAI, Anthropic, Google, and xAI
• Streaming Support: Real-time streaming responses from all providers
• Model Comparison: Compare responses from multiple models simultaneously
• Content Analysis: Analyze code, text, security, and performance
• Content Generation: Generate code, documentation, and tests
• Automatic Retry: Built-in retry logic with exponential backoff
• Error Handling: Comprehensive error handling across all providers

Installation

Quick Install

Choose your preferred installation method:

Using NPX (Recommended)

npx @physics91org/ai-api-mcp

Using Bun

bunx @physics91org/ai-api-mcp

Using Docker

docker run -it --rm \
  -e OPENAI_API_KEY=your_key \
  -e ANTHROPIC_API_KEY=your_key \
  -e GOOGLE_API_KEY=your_key \
  -e GROK_API_KEY=your_key \
  ai-api-mcp

Using Docker Compose

# Clone the repository first
git clone https://github.com/yourusername/ai-api-mcp.git
cd ai-api-mcp

# Copy and edit .env file
cp .env.example .env

# Run with docker-compose
docker-compose up

Manual Installation

Prerequisites

• Python 3.10 or higher
• pip

Steps

1. Clone the repository:

git clone https://github.com/yourusername/ai-api-mcp.git
cd ai-api-mcp

2. Run the installation script:

Linux/macOS:

chmod +x install.sh
./install.sh

Windows:

python -m venv venv
venv\Scripts\activate
pip install -e .

3. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys

Development Installation

For development with hot-reload and editable installation:

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Linux/macOS:
source venv/bin/activate
# Windows:
venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

Configuration

Add your API keys to the .env file:

# AI API Keys
OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GOOGLE_API_KEY=your_google_api_key_here
GROK_API_KEY=your_grok_api_key_here

# Optional: Custom API endpoints
# OPENAI_BASE_URL=https://api.openai.com/v1
# GROK_BASE_URL=https://api.x.ai/v1

# Retry Configuration
MAX_RETRIES=3
RETRY_DELAY=1.0

Usage

Running the Server

Choose your preferred method to run the server:

Using NPX/Bunx (No installation required)

# With npx
npx @physics91org/ai-api-mcp

# With bunx  
bunx @physics91org/ai-api-mcp

Using Node.js

npm start
# or
node run.js

Using Python

python -m src.server

Using Shell Script

./run.sh

Using Docker

# Build and run
docker build -t ai-api-mcp .
docker run -it --rm --env-file .env ai-api-mcp

# Or use docker-compose
docker-compose up

Available Tools

1. Chat

Send messages to AI models and get responses.

await mcp.chat(
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello!"}
    ],
    model="gpt-4",
    temperature=0.7,
    max_tokens=1000
)

2. List Models

Get all available models from configured providers.

models = await mcp.list_models()

3. Compare

Compare responses from multiple models.

await mcp.compare(
    prompt="Explain quantum computing",
    models=["gpt-4", "claude-3-opus-20240229", "gemini-pro"],
    temperature=0.7
)

4. Analyze

Analyze content with specific focus.

await mcp.analyze(
    content="def factorial(n): return 1 if n <= 1 else n * factorial(n-1)",
    analysis_type="code",  # options: code, text, security, performance, general
    model="gpt-4"
)

5. Generate

Generate content of specific types.

await mcp.generate(
    prompt="Create a REST API for user management",
    generation_type="code",  # options: code, text, documentation, test
    model="gpt-4",
    language="python",
    framework="FastAPI"
)

Supported Models (2025)

OpenAI

Flagship GPT Models

• gpt-4.1 - 1M context, multimodal with massive context
• gpt-4o - 128K context, fast, intelligent, flexible
• gpt-4o-audio-preview - 128K context, audio inputs/outputs
• chatgpt-4o-latest - 128K context, ChatGPT version

Cost-Optimized Models

• gpt-4.1-mini - 1M context, fast multimodal
• gpt-4.1-nano - 1M context, ultra-fast
• gpt-4o-mini - 128K context, fast and affordable
• gpt-4o-mini-audio-preview - 128K context, audio support

Reasoning Models (o-series)

• o4-mini - 200K context, faster reasoning
• o3 - 200K context, most powerful reasoning
• o3-pro - 200K context, deep thinking
• o3-mini - 200K context, small reasoning alternative
• o1 - 200K context, previous reasoning model
• o1-mini - 128K context, small reasoning alternative
• o1-pro - 200K context, enhanced reasoning

Older Models

• gpt-4-turbo, gpt-4, gpt-3.5-turbo

Anthropic

Claude 4 Models (Latest Generation)

• claude-opus-4-20250514 - Most powerful and capable model (32K output)
• claude-sonnet-4-20250514 - High-performance with exceptional reasoning (64K output)

Claude 3.x Models

• claude-3-7-sonnet-20250219 - High intelligence with extended thinking (64K output)
• claude-3-5-sonnet-20241022 - Previous intelligent model v2 (8K output)
• claude-3-5-sonnet-20240620 - Previous intelligent model (8K output)
• claude-3-5-haiku-20241022 - Fastest model with intelligence (8K output)
• claude-3-haiku-20240307 - Fast and compact for quick responses (4K output)

Google

Gemini 2.5 Series (Latest with Thinking)

• gemini-2.5-pro - 1M context, advanced reasoning with deep thinking
• gemini-2.5-flash - 1M context, fast advanced reasoning with thinking
• gemini-2.5-flash-lite-preview-06-17 - 1M context, ultra-fast and cost-effective

Gemini 2.0 Series

• gemini-2.0-flash - 1M context, real-time multimodal capabilities
• gemini-2.0-flash-lite - 1M context, cost-effective and fast

Gemini 1.5 Series (Deprecated)

• gemini-1.5-flash - 1M context, fast multimodal (deprecated)
• gemini-1.5-flash-8b - 1M context, high volume processing (deprecated)
• gemini-1.5-pro - 2M context, complex reasoning (deprecated)

xAI

Grok 4 Series (Latest Reasoning Models)

• grok-4-0709 - 256K context, advanced reasoning with function calling

Grok 3 Series

• grok-3 - 131K context, vision and function calling capabilities
• grok-3-mini - 131K context, fast and efficient reasoning
• grok-3-fast - 131K context, high-speed processing with regional availability
• grok-3-mini-fast - 131K context, ultra-fast efficient processing

Grok 2 Series (Vision Models)

• grok-2-vision-1212 - 32K context, vision capabilities with function calling

MCP Client Support

This server works with multiple MCP-supporting tools. See our MCP Installation Guide for detailed setup instructions.

Supported Clients

• Claude Code (CLI) - Anthropic's official CLI with MCP support
• Claude Desktop - Native desktop app with MCP integration
• Cursor IDE - AI-powered IDE with built-in MCP support
• VS Code - Via GitHub Copilot Chat extension
• Windsurf Editor - Next-gen editor with MCP capabilities
• Continue Extension - Open-source AI code assistant
• And more...

Quick Configuration Example

{
  "mcpServers": {
    "ai-api": {
      "command": "npx",
      "args": ["@physics91org/ai-api-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-key",
        "ANTHROPIC_API_KEY": "your-key",
        "GOOGLE_API_KEY": "your-key",
        "GROK_API_KEY": "your-key"
      }
    }
  }
}

Development

Project Structure

ai-api-mcp/
├── src/
│   ├── server.py           # FastMCP server implementation
│   ├── provider_manager.py # Manages all AI providers
│   ├── models.py          # Pydantic models
│   ├── utils.py           # Utility functions
│   └── providers/         # AI provider implementations
│       ├── base.py
│       ├── openai_provider.py
│       ├── gemini_provider.py
│       ├── anthropic_provider.py
│       └── grok_provider.py
├── .env.example
├── pyproject.toml
└── README.md

Adding New Providers

1. Create a new provider class in src/providers/
2. Inherit from AIProviderBase
3. Implement required methods: chat, list_models, validate_model
4. Add provider to ProviderManager in provider_manager.py

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.