Converse MCP Enhanced

Multi-model AI conversation MCP for Claude Desktop. Seamlessly integrate with GPT-4, Gemini, xAI, Perplexity, and local models via Ollama. Achieve 80-95% cost savings through intelligent routing and local model prioritization.

🌟 Features

• 10+ AI Models: GPT-4, Claude, Gemini, xAI Grok, Perplexity, Ollama

• 80-95% Cost Savings: Smart routing prioritizes local models via Ollama

• Parallel Processing: Query multiple models simultaneously for consensus

• Web Search Integration: Real-time information via Perplexity Sonar

• Conversation Memory: Persistent context across sessions

• Smart Fallbacks: Automatic failover to ensure reliability

• Zero Setup Ollama: Auto-detection and configuration

Related MCP server: 1mcpserver

🚀 Supported Models

API Models

• OpenAI: GPT-4, GPT-3.5 Turbo

• Anthropic: Claude 3 Opus, Sonnet, Haiku

• Google: Gemini Pro, Gemini Ultra

• xAI: Grok-1, Grok-2

• Perplexity: Sonar (with web search)

Local Models (via Ollama)

• Llama 3 (8B, 70B)

• Mistral, Mixtral

• CodeLlama

• Phi-2

• Neural Chat

• Any Ollama-compatible model

💰 Cost Optimization Strategy

Priority Order (Highest to Lowest):
1. Ollama local models (FREE) - ALWAYS FIRST
2. Intelligent model selection based on query
3. API models only as fallback

Result: 100% FREE operation when Ollama available

🧠 Smart Model Selection (v1.1.0)

The system automatically selects the optimal Ollama model:

• Simple queries (<50 chars) → phi3:mini (fastest)

• Code-related → codellama:7b

• Complex queries (>200 chars) → qwen2.5-coder:32b

• Default → llama3.2:3b (balanced)

📦 Installation

Via NPM (Recommended)

npm install -g converse-mcp-enhanced

Manual Installation

git clone https://github.com/justmy2satoshis/converse-mcp-enhanced.git
cd converse-mcp-enhanced
npm install

🔧 Configuration

Add to your Claude Desktop configuration file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "converse": {
      "command": "node",
      "args": ["C:\\path\\to\\converse-mcp-enhanced\\src\\server.js"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GOOGLE_API_KEY": "...",
        "XAI_API_KEY": "...",
        "PERPLEXITY_API_KEY": "...",
        "OLLAMA_HOST": "http://localhost:11434"
      }
    }
  }
}

📖 Usage Examples

Single Model Query

const response = await chat({
  model: "auto",  // Automatic model selection
  prompt: "Explain quantum computing",
  temperature: 0.7
});

Multi-Model Consensus

const consensus = await consensus({
  models: ["gpt-4", "gemini-pro", "llama3"],
  prompt: "Should we use microservices architecture?",
  enable_cross_feedback: true
});

Web Search Integration

const results = await chat({
  model: "perplexity",
  prompt: "Latest developments in AI safety",
  use_websearch: true
});

Cost-Optimized Query

const response = await chat({
  model: "auto",
  prompt: "Generate unit tests for this function",
  prefer_local: true,  // Prioritize Ollama models
  max_cost: 0.01      // Cost limit in USD
});

💡 Use Cases

Development Assistance

• Code generation with local models (free)

• Multi-model code review for quality

• Documentation with web search context

Research & Analysis

• Consensus building across models

• Real-time information via Perplexity

• Complex reasoning with chain-of-thought

Cost Management

• 95% of queries handled by Ollama

• API models only for specialized tasks

• Automatic caching reduces redundant calls

🏗️ Architecture

converse-mcp-enhanced/
├── src/
│   ├── server.js           # Main MCP server
│   ├── models/
│   │   ├── router.js       # Intelligent routing
│   │   ├── ollama.js       # Ollama integration
│   │   └── providers/      # API integrations
│   ├── consensus.js        # Multi-model consensus
│   └── cache.js            # Response caching
├── examples/               # Usage examples
├── tests/                  # Test suite
└── package.json

📊 Performance Metrics

🧪 Testing

npm test

Tests include:

• Model routing logic

• Cost optimization algorithms

• Consensus mechanisms

• Fallback scenarios

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Priority Areas

1. Additional model providers

2. Enhanced caching strategies

3. Cost optimization improvements

4. Consensus algorithms

🔒 Security

• API keys stored securely in environment

• No logging of sensitive data

• Local model priority reduces data exposure

• Optional request encryption

📝 License

MIT License - see LICENSE file for details

🙏 Acknowledgments

• Anthropic for Model Context Protocol

• Ollama team for local model infrastructure

• OpenAI, Google, xAI, Perplexity for APIs

• Community contributors

📧 Support

• Issues: GitHub Issues

• Discussions: GitHub Discussions

🚦 Status

• ✅ Production Ready

• ✅ 10+ models integrated

• ✅ Ollama auto-configuration

• ✅ Cost optimization active

• ✅ Claude Desktop compatible

Note: Requires Claude Desktop with MCP support. Ollama recommended for maximum cost savings.

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