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deepseek-mcp-server - npm

A Model Context Protocol (MCP) server for the DeepSeek API, allowing seamless integration of DeepSeek's powerful language models with MCP-compatible applications like Claude Desktop.

To install DeepSeek MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @dmontgomery40/deepseek-mcp-server --client claude

npm install -g deepseek-mcp-server

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "deepseek": {
      "command": "npx",
      "args": [
        "-y",
        "deepseek-mcp-server"
      ],
      "env": {
        "DEEPSEEK_API_KEY": "your-api-key"
      }
    }
  }
}

Note: The server intelligently handles these natural language requests by mapping them to appropriate configuration changes. You can also query the current settings and available models:

• User: "What models are available?"
  • Response: Shows list of available models and their capabilities via the models resource.
• User: "What configuration options do I have?"
  • Response: Lists all available configuration options via the model-config resource.
• User: "What is the current temperature setting?"
  • Response: Displays the current temperature setting.
• User: "Start a multi-turn conversation. With the following settings: model: 'deepseek-chat', make it not too creative, and allow 8000 tokens."
  • Response: Starts a multi-turn conversation with the specified settings.

• If the primary model (R1) is down (called deepseek-reasoner in the server), the server will automatically attempt to try with v3 (called deepseek-chat in the server)

Note: You can switch back and forth anytime as well, by just giving your prompt and saying "use deepseek-reasoner" or "use deepseek-chat"

• V3 is recommended for general purpose use, while R1 is recommended for more technical and complex queries, primarily due to speed and token useage

• Custom model selection
• Temperature control (0.0 - 2.0)
• Max tokens limit
• Top P sampling (0.0 - 1.0)
• Presence penalty (-2.0 - 2.0)
• Frequency penalty (-2.0 - 2.0)

Multi-turn conversation support:

• Maintains complete message history and context across exchanges
• Preserves configuration settings throughout the conversation
• Handles complex dialogue flows and follow-up chains automatically

This feature is particularly valuable for two key use cases:

1. Training & Fine-tuning: Since DeepSeek is open source, many users are training their own versions. The multi-turn support provides properly formatted conversation data that's essential for training high-quality dialogue models.

2. Complex Interactions: For production use, this helps manage longer conversations where context is crucial:

   • Multi-step reasoning problems
   • Interactive troubleshooting sessions
   • Detailed technical discussions
   • Any scenario where context from earlier messages impacts later responses

The implementation handles all context management and message formatting behind the scenes, letting you focus on the actual interaction rather than the technical details of maintaining conversation state.

You can test the server locally using the MCP Inspector tool:

1. Build the server:

2. Run the server with MCP Inspector:

   # Make sure to specify the full path to the built server
   npx @modelcontextprotocol/inspector node ./build/index.js

The inspector will open in your browser and connect to the server via stdio transport. You can:

• View available tools
• Test chat completions with different parameters
• Debug server responses
• Monitor server performance

Note: The server uses DeepSeek's R1 model (deepseek-reasoner) by default, which provides state-of-the-art performance for reasoning and general tasks.

MIT

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