RAT MCP Server: Retrieval Augmented Thinking

Overview: What is RAT MCP Server?

RAT (Retrieval Augmented Thinking) MCP Server is an advanced Model Context Protocol (MCP) server designed to deliver a seamless integration experience between AI applications and external data sources or tools. By combining the powerful reasoning capabilities of DeepSeek with multiple response models, it ensures that AI applications can efficiently process complex queries while maintaining contextual integrity throughout the conversation.

This server not only supports Claude 3.5 Sonnet via Anthropic but also includes compatibility with various OpenRouter models (GPT-4, Gemini), making it a versatile tool for developers looking to enhance their AI products. By leveraging MCP, the RAT server can seamlessly integrate into popular AI clients such as Claude Desktop, Continue, and Cursor, enabling richer conversations and more accurate responses.

🔧 Core Features & MCP Capabilities

Two-Stage Processing

The RAT MCP Server implements a two-stage processing mechanism that significantly enhances reasoning accuracy:

1. DeepSeek Reasoning: The DeepSeek API is employed to conduct detailed and structured analysis of input queries.
2. Multiple Models for Response Generation: Post-reasoning, the server uses one or more models (Claude 3.5 Sonnet or any OpenRouter model) to generate comprehensive responses.

This bi-layered approach ensures both robust reasoning and thoughtful response generation, providing users with highly relevant and contextually accurate outputs.

Context Management

Effective context management is crucial for maintaining conversation flow:

1. Conversation History: The server preserves the history of previous interactions, enabling each new query to build upon previously answered questions.
2. Incorporating Previous Q&A in Reasoning: By including past exchanges during reasoning, the server can provide more coherent and informed responses.
3. Context Clraring When Needed: Users have the option to clear out old context, allowing for a fresh start when necessary.
4. Configurable Context Limits: Administrators can set limits on how much historical data is retained, balancing between thoroughness and performance.

⚙️ MCP Architecture & Protocol Implementation

The RAT MCP Server operates within a structured framework adhering to Model Context Protocol (MCP) standards. Here’s an overview of its key components:

• Client Applications: Compatible with AI clients like Claude Desktop, Continue, Cursor.
• Server Core: Handles logical flow and data processing.
• Data Interactions: Mediates communication between DeepSeek for analysis and various response models.

MERMAID Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MERMAID Diagram: Data Architecture

graph TD
  start-->|Query| B[Client]
  B -->|MCP Message| C[MCP Server]
  C -->|Analysis & Response| D[Data Source/Tool]
  D --- E[Response] --> F[Client UI]

🚀 Getting Started with Installation

To get started, follow these steps:

1. Clone Repository and Navigate:

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

2. Install Dependencies:

   npm install
   

3. Create Environment File: Configure API keys and model settings in a .env file.

   DEEPSEEK_API_KEY=your_deepseek_api_key_here
   OPENROUTER_API_KEY=your_openrouter_api_key_here
   ANTHROPIC_API_KEY=your_anthropic_api_key_here
   DEFAULT_MODEL=claude-3-5-sonnet-20241022  # or any OpenRouter model ID
   OPENROUTER_MODEL=openai/gpt-4  # default OpenRouter model if not using Claude
   

4. Build the Server:

   npm run build
   

💡 Key Use Cases in AI Workflows

Real-Time Query Analysis

In a fast-paced support environment, users can pose complex questions and receive detailed analysis followed by contextually rich responses. This capability is particularly useful for help desks or customer service departments where quick and informed assistance is critical.

Custom Knowledge Base Integration

For organizations with extensive knowledge bases, the RAT MCP Server allows AI tools to cross-reference these resources against user queries. By using DeepSeek's reasoning capabilities, it ensures that only relevant parts of the vast repository are accessed and presented to users, improving efficiency and accuracy.

🔌 Integration with MCP Clients

Integrate the RAT MCP Server into popular AI clients for enhanced functionality:

1. Claude Desktop:

   {
     "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": []
       }
     }
   }
   

2. Continue:

   {
     "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": []
       }
     }
   }
   

📊 Performance & Compatibility Matrix

MCP Client Compatibility

MPC Client	Claude Desktop	Continue	Cursor
Tools	✅	✅	❌
Prompts	✅	✅	❌
Resources	✅	✅	❌

Key Considerations

• Claude Desktop: Full support for tool interactions and knowledge prompts.
• Continue: Similar to Claude but with some limitations in tool context handling.

🛠️ Advanced Configuration & Security

Advanced configurations include:

1. API Keys Management: Properly secure and manage API keys across different environments.
2. Context Limits: Control the amount of context retained for each session.
3. Custom Model Configurations: Implement fallbacks or custom models as needed.

Sample Configuration Code

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

❓ Frequently Asked Questions (FAQ)

1. How does the RAT MCP Server improve AI response accuracy?

   • By leveraging DeepSeek for detailed analysis and multiple models for refining responses, it ensures that outputs are both thorough and contextually relevant.

2. Can I use my own response models with the RAT MCP Server?

   • Yes, you can integrate any OpenRouter model into your configurations to tailor the server to specific needs.

3. Is this suitable for large-scale deployment in enterprise settings?

   • Absolutely. The robust architecture of the RAT MCP Server supports extensive data processing and context management necessary for enterprise applications.

4. Can I switch between different models dynamically during execution?

   • The system allows dynamic switching, but it requires reconfiguration of the environment file or client settings.

5. Does the server support real-time updates from external sources?

   • While primarily focused on static resource integration, you can implement real-time data fetching through additional APIs and webhooks.

👨‍💻 Development & Contribution Guidelines

Contributors are welcome to enhance the functionality of the RAT MCP Server:

1. Code Contributions: Pull requests for bug fixes or new features are highly appreciated.
2. Documentation Updates: Improving existing documentation and adding new sections as needed.
3. Testing: Help in ensuring robustness through thorough testing.

Repository

For updates and contributions, visit the repository: RAT MCP Server GitHub

🌐 MCP Ecosystem & Resources

Explore more about Model Context Protocol (MCP):

• Official Documentation: Detailed guides on setting up and integrating with MCP clients.
• Community Forums: Engage with other users and developers for support and collaboration.

By following these guidelines, you can effectively leverage the power of the RAT MCP Server to enhance AI applications, ensuring better integration, accuracy, and user experience.