RAT MCP Server

# Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP [](https://smithery.ai/server/@newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP) A Model Context Protocol (MCP) server that combines DeepSeek R1's reasoning capabilities with Claude 3.5 Sonnet's response generation through OpenRouter. This implementation uses a two-stage process where DeepSeek provides structured reasoning which is then incorporated into Claude's response generation. ## Features - **Two-Stage Processing**: - Uses DeepSeek R1 for initial reasoning (50k character context) - Uses Claude 3.5 Sonnet for final response (600k character context) - Both models accessed through OpenRouter's unified API - Injects DeepSeek's reasoning tokens into Claude's context - **Smart Conversation Management**: - Detects active conversations using file modification times - Handles multiple concurrent conversations - Filters out ended conversations automatically - Supports context clearing when needed - **Optimized Parameters**: - Model-specific context limits: * DeepSeek: 50,000 characters for focused reasoning * Claude: 600,000 characters for comprehensive responses - Recommended settings: * temperature: 0.7 for balanced creativity * top_p: 1.0 for full probability distribution * repetition_penalty: 1.0 to prevent repetition ## Installation ### Installing via Smithery To install DeepSeek Thinking with Claude 3.5 Sonnet for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP): ```bash npx -y @smithery/cli install @newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP --client claude ``` ### Manual Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP.git cd Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP ``` 2. Install dependencies: ```bash npm install ``` 3. Create a `.env` file with your OpenRouter API key: ```env # Required: OpenRouter API key for both DeepSeek and Claude models OPENROUTER_API_KEY=your_openrouter_api_key_here # Optional: Model configuration (defaults shown below) DEEPSEEK_MODEL=deepseek/deepseek-r1 # DeepSeek model for reasoning CLAUDE_MODEL=anthropic/claude-3.5-sonnet:beta # Claude model for responses ``` 4. Build the server: ```bash npm run build ``` ## Usage with Cline Add to your Cline MCP settings (usually in `~/.vscode/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`): ```json { "mcpServers": { "deepseek-claude": { "command": "/path/to/node", "args": ["/path/to/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP/build/index.js"], "env": { "OPENROUTER_API_KEY": "your_key_here" }, "disabled": false, "autoApprove": [] } } } ``` ## Tool Usage The server provides two tools for generating and monitoring responses: ### generate_response Main tool for generating responses with the following parameters: ```typescript { "prompt": string, // Required: The question or prompt "showReasoning"?: boolean, // Optional: Show DeepSeek's reasoning process "clearContext"?: boolean, // Optional: Clear conversation history "includeHistory"?: boolean // Optional: Include Cline conversation history } ``` ### check_response_status Tool for checking the status of a response generation task: ```typescript { "taskId": string // Required: The task ID from generate_response } ``` ### Response Polling The server uses a polling mechanism to handle long-running requests: 1. Initial Request: - `generate_response` returns immediately with a task ID - Response format: `{"taskId": "uuid-here"}` 2. Status Checking: - Use `check_response_status` to poll the task status - **Note:** Responses can take up to 60 seconds to complete - Status progresses through: pending → reasoning → responding → complete Example usage in Cline: ```typescript // Initial request const result = await use_mcp_tool({ server_name: "deepseek-claude", tool_name: "generate_response", arguments: { prompt: "What is quantum computing?", showReasoning: true } }); // Get taskId from result const taskId = JSON.parse(result.content[0].text).taskId; // Poll for status (may need multiple checks over ~60 seconds) const status = await use_mcp_tool({ server_name: "deepseek-claude", tool_name: "check_response_status", arguments: { taskId } }); // Example status response when complete: { "status": "complete", "reasoning": "...", // If showReasoning was true "response": "..." // The final response } ``` ## Development For development with auto-rebuild: ```bash npm run watch ``` ## How It Works 1. **Reasoning Stage (DeepSeek R1)**: - Uses OpenRouter's reasoning tokens feature - Prompt is modified to output 'done' while capturing reasoning - Reasoning is extracted from response metadata 2. **Response Stage (Claude 3.5 Sonnet)**: - Receives the original prompt and DeepSeek's reasoning - Generates final response incorporating the reasoning - Maintains conversation context and history ## License MIT License - See LICENSE file for details. ## Credits Based on the RAT (Retrieval Augmented Thinking) concept by [Skirano](https://x.com/skirano/status/1881922469411643413), which enhances AI responses through structured reasoning and knowledge retrieval. This implementation specifically combines DeepSeek R1's reasoning capabilities with Claude 3.5 Sonnet's response generation through OpenRouter's unified API.