OpenAI MCP Server

Advanced OpenAI GPT-5 MCP Server with multiple tools, intelligent reasoning, and comprehensive AI utilities for Claude Code integration.

✨ Features

🎯 Multiple AI Tools

• 5 Specialized Tools: From basic GPT calling to advanced batch processing
• Modular Architecture: Each tool is independently developed and maintained
• Extensible Framework: Easy to add new tools and capabilities

🧠 Advanced AI Capabilities

• GPT-5 by Default: Latest reasoning model with intelligent fallback to GPT-4o
• Advanced Reasoning: Support for reasoning_effort and verbosity parameters
• Hybrid Intelligence: GPT-5 reasoning + GPT-4o content generation
• Task-Specific Optimization: Specialized system prompts for different domains

🚀 Professional Features

• Token Analysis & Optimization: Analyze and optimize text for token efficiency
• Context Window Management: Smart context optimization with multiple strategies
• Batch Processing: Process multiple prompts in parallel with concurrency control
• Model Management: List and compare available OpenAI models

🔧 Technical Excellence

• stdio Transport: No ports needed, simple integration
• Claude Context Integration: Leverages Claude session context
• NPX Ready: Install and run with npx
• TypeScript: Full type safety and modern JavaScript features
• Error Handling: Comprehensive error handling and fallback mechanisms

Quick Start

Installation

# Global install
npm install -g openai-mcp-server

# Or use npx (no installation needed)
npx openai-mcp-server

Setup

1. Get your OpenAI API key from https://platform.openai.com/api-keys

2. Set environment variable:

export OPENAI_API_KEY="your-api-key-here"

3. Add to Claude Code:

claude mcp add --transport stdio openai-gpt5 \\
  "OPENAI_API_KEY=your-key-here npx openai-mcp-server"

Available Tools

The server provides 5 specialized tools:

1. call_gpt5 - Enhanced GPT Model Calling

Call OpenAI GPT models with optimized system prompts and advanced reasoning.

Key Parameters:

• prompt (string, required): Your question or request
• taskType (enum, required): analysis, generation, reasoning, coding
• domain (string, optional): Specific domain like "security", "performance", "architecture"
• reasoningEffort (enum, optional): GPT-5 reasoning depth - "minimal", "low", "medium", "high"
• verbosity (enum, optional): GPT-5 response detail level - "low", "medium", "high"
• model (string, optional): Override model ("gpt-5", "gpt-4o", "gpt-4")

2. list_models - Model Information

List available OpenAI models with capabilities and metadata.

Parameters:

• includeDetails (boolean, optional): Include detailed model information

3. analyze_token_usage - Token Optimization

Analyze text for token usage and get optimization suggestions.

Parameters:

• text (string, required): Text to analyze
• model (string, optional): Model for token counting
• includeOptimization (boolean, optional): Include optimization suggestions

Features:

• Token count estimation
• Text composition analysis
• Cost calculation
• Optimization recommendations

4. optimize_context_window - Context Management

Optimize long context for efficient token usage while preserving important information.

Parameters:

• context (string, required): Context text to optimize
• maxTokens (number, required): Maximum tokens for optimized context
• preservationStrategy (enum, optional): Strategy for preserving context
  • important_first: Preserve sentences with keywords and importance indicators
  • recent_first: Preserve recent content with keyword protection
  • semantic: Preserve semantically similar content
  • balanced: Balance importance and recency (default)
• preserveKeywords (array, optional): Keywords to preserve

Use Cases:

• Large document summarization
• Chat history optimization
• Context window management

5. process_batch_prompts - Batch Processing

Process multiple prompts efficiently with parallel execution support.

Parameters:

• prompts (array, required): Array of prompts to process
• taskType (enum, optional): Task type for system prompt optimization
• parallel (boolean, optional): Process prompts in parallel (default: true)
• maxConcurrency (number, optional): Maximum concurrent requests (1-10, default: 5)
• model (string, optional): Model to use for all prompts

Features:

• Parallel processing with concurrency control
• Automatic retry and error handling
• Performance metrics and cost estimation
• Progress tracking

Usage Examples

# Basic GPT-5 call with reasoning
claude mcp call openai-gpt5 call_gpt5 '{
  "prompt": "Analyze this code for security vulnerabilities",
  "taskType": "analysis", 
  "domain": "security",
  "reasoningEffort": "high"
}'

# Token analysis
claude mcp call openai-gpt5 analyze_token_usage '{
  "text": "Your text here...",
  "includeOptimization": true
}'

# Batch processing
claude mcp call openai-gpt5 process_batch_prompts '{
  "prompts": ["Question 1", "Question 2", "Question 3"],
  "parallel": true,
  "maxConcurrency": 3
}'

# Context optimization
claude mcp call openai-gpt5 optimize_context_window '{
  "context": "Very long text...",
  "maxTokens": 1000,
  "preservationStrategy": "important_first",
  "preserveKeywords": ["key", "important"]
}'

Environment Variables

Create a .env file or set environment variables:

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional  
OPENAI_MODEL=gpt-5                    # Default model (gpt-5, gpt-4o, gpt-4)
OPENAI_BASE_URL=https://api.openai.com/v1  # Custom API endpoint
DEBUG=false                           # Enable debug logging

Development

# Clone and install
git clone <repository>
cd openai-mcp-server
npm install

# Build
npm run build

# Development with auto-reload
npm run dev

# Test
npm test

Integration with Claude Code

Once configured, Claude can automatically use this comprehensive MCP server to:

🎯 Core AI Capabilities

1. Enhanced Analysis: Deep code analysis with GPT-5 reasoning capabilities
2. Alternative Perspectives: Get different AI viewpoints on complex problems
3. Creative Problem Solving: Leverage GPT's creativity for brainstorming and innovation
4. Specialized Domain Expertise: Task-specific optimized prompts for security, performance, architecture

🚀 Advanced Features

5. Token Optimization: Analyze and optimize prompts for cost-effectiveness
6. Context Management: Handle large documents and conversations efficiently
7. Batch Operations: Process multiple requests simultaneously for productivity
8. Model Selection: Choose optimal models based on task requirements

💡 Smart Integration

Claude will intelligently decide when and which tools to use based on:

• Task complexity and type
• Content length and optimization needs
• Batch processing opportunities
• Resource and cost considerations

🔧 Professional Workflows

• Development: Code analysis, review, and optimization
• Content: Large document processing and summarization
• Research: Multi-query analysis and comparison
• Optimization: Token usage and cost management

License

MIT