RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling

RAT MCP Server seamlessly blends DeepSeek’s reasoning with GPT-4/Claude/Mistral’s responses, preserving interaction context for fluid, multi-turn conversational AI at enterprise scale.

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About RAT MCP Server

What is RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling?

RAT MCP Server is an advanced AI infrastructure designed to enhance conversational AI systems through structured reasoning and scalable context management. By integrating DeepSeek's analytical capabilities with multiple response models, it enables fluid, multi-turn interactions while maintaining enterprise-grade context handling. This server acts as a bridge between raw thinking processes and polished final outputs, ensuring both depth and adaptability in AI-driven dialogues.

How to Use RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling?

Clone the repository and install dependencies via npm
Configure API keys and model preferences in the .env file
Build and run the server
Integrate with Cline by updating MCP settings with your environment variables
Invoke the generate_response tool specifying prompts, context preferences, and desired models

Example workflow: Use the server to process multi-step queries where prior context influences subsequent responses, like troubleshooting workflows or dynamic recommendation systems.

RAT MCP Server Features

Key Features of RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling?

Two-Stage Processing: Separates raw analytical thinking from finalized outputs for better control
Multi-Model Flexibility: Supports DeepSeek, LLMs, and custom models through modular architecture
Enterprise Context Management: Tracks conversation history, allows dynamic context scaling, and provides granular control over memory retention
API-First Design: Exposes clean endpoints for easy integration with existing platforms

Use Cases of RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling?

Common applications include:

Complex customer service chatbots requiring memory of multi-turn interactions
Technical support systems where prior troubleshooting steps influence next actions
Enterprise knowledge bases needing adaptive context awareness across user sessions
AI-driven platforms requiring model switching based on conversation stage

RAT MCP Server FAQ

FAQ from RAT MCP Server: Fluid Multi-Turn AI & Enterprise Context Scaling?

Q: Does the server support real-time context updates?

A: Yes - context management APIs allow dynamic adjustments during conversations

Q: Can I use custom models alongside DeepSeek?

A: Absolutely. The modular architecture supports adding new models via plugin interfaces

Q: What happens if API keys are missing during runtime?

A: The server will fail gracefully with clear error logging to identify configuration gaps

Q: How is context data secured?

A: Context storage and transmission use encryption by default, with optional RBAC controls for enterprise deployments

Content

RAT MCP Server (Retrieval Augmented Thinking)

A Model Context Protocol (MCP) server that implements RAT's two-stage reasoning process, combining DeepSeek's reasoning capabilities with various response models.

Features

Two-Stage Processing :
- Uses DeepSeek for detailed reasoning and analysis
- Supports multiple models for final response generation
- Maintains conversation context between interactions
Supported Models :
- DeepSeek Reasoner (for thinking process)
- Claude 3.5 Sonnet (via Anthropic)
- Any OpenRouter model (GPT-4, Gemini, etc.)
Context Management :
- Maintains conversation history
- Includes previous Q&A in reasoning process
- Supports context clearing when needed
- Configurable context size limit

Installation

Clone the repository:

git clone https://github.com/newideas99/RAT-retrieval-augmented-thinking-MCP.git
cd rat-mcp-server

Install dependencies:

npm install

Create a .env file with your API keys and model configuration:

# Required: DeepSeek API key for reasoning stage
DEEPSEEK_API_KEY=your_deepseek_api_key_here

# Required: OpenRouter API key for non-Claude models
OPENROUTER_API_KEY=your_openrouter_api_key_here

# Optional: Anthropic API key for Claude model
ANTHROPIC_API_KEY=your_anthropic_api_key_here

# Optional: Model configuration
DEFAULT_MODEL=claude-3-5-sonnet-20241022  # or any OpenRouter model ID
OPENROUTER_MODEL=openai/gpt-4  # default OpenRouter model if not using Claude

Build the server:

npm run build

Usage with Cline

Add to your Cline MCP settings (usually in ~/.vscode/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json):

{
  "mcpServers": {
    "rat": {
      "command": "/path/to/node",
      "args": ["/path/to/rat-mcp-server/build/index.js"],
      "env": {
        "DEEPSEEK_API_KEY": "your_key_here",
        "OPENROUTER_API_KEY": "your_key_here",
        "ANTHROPIC_API_KEY": "your_key_here",
        "DEFAULT_MODEL": "claude-3-5-sonnet-20241022",
        "OPENROUTER_MODEL": "openai/gpt-4"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Tool Usage

The server provides a single tool generate_response with the following parameters:

{
  "prompt": string,           // Required: The question or prompt
  "showReasoning"?: boolean, // Optional: Show DeepSeek's reasoning process
  "clearContext"?: boolean   // Optional: Clear conversation history
}

Example usage in Cline:

use_mcp_tool({
  server_name: "rat",
  tool_name: "generate_response",
  arguments: {
    prompt: "What is Python?",
    showReasoning: true
  }
});

Development

For development with auto-rebuild:

npm run watch

License

MIT License - See LICENSE file for details.

Credits

Based on the RAT (Retrieval Augmented Thinking) concept by Skirano, which enhances AI responses through structured reasoning and knowledge retrieval.