深度求索R1
STDIO用于Deepseek R1大语言模型的MCP服务器
用于Deepseek R1大语言模型的MCP服务器
A Model Context Protocol (MCP) server implementation for the Deepseek R1 language model. Deepseek R1 is a powerful language model optimized for reasoning tasks with a context window of 8192 tokens.
Why Node.js? This implementation uses Node.js/TypeScript as it provides the most stable integration with MCP servers. The Node.js SDK offers better type safety, error handling, and compatibility with Claude Desktop.
# Clone and install git clone https://github.com/66julienmartin/MCP-server-Deepseek_R1.git cd deepseek-r1-mcp npm install # Set up environment cp .env.example .env # Then add your API key # Build and run npm run build
By default, this server uses the deepseek-R1 model. If you want to use DeepSeek-V3 instead, modify the model name in src/index.ts
:
// For DeepSeek-R1 (default) model: "deepseek-reasoner" // For DeepSeek-V3 model: "deepseek-chat"
deepseek-r1-mcp/
├── src/
│ ├── index.ts # Main server implementation
├── build/ # Compiled files
│ ├── index.js
├── LICENSE
├── README.md
├── package.json
├── package-lock.json
└── tsconfig.json
.env
file:DEEPSEEK_API_KEY=your-api-key-here
{ "mcpServers": { "deepseek_r1": { "command": "node", "args": ["/path/to/deepseek-r1-mcp/build/index.js"], "env": { "DEEPSEEK_API_KEY": "your-api-key" } } } }
npm run dev # Watch mode npm run build # Build for production
{ "name": "deepseek_r1", "arguments": { "prompt": "Your prompt here", "max_tokens": 8192, // Maximum tokens to generate "temperature": 0.2 // Controls randomness } }
The default value of temperature
is 0.2.
Deepseek recommends setting the temperature
according to your specific use case:
USE CASE | TEMPERATURE | EXAMPLE |
---|---|---|
Coding / Math | 0.0 | Code generation, mathematical calculations |
Data Cleaning / Data Analysis | 1.0 | Data processing tasks |
General Conversation | 1.3 | Chat and dialogue |
Translation | 1.3 | Language translation |
Creative Writing / Poetry | 1.5 | Story writing, poetry generation |
The server provides detailed error messages for common issues:
Contributions are welcome! Please feel free to submit a Pull Request.
MIT