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DeepSeek MCP Server

Model Context Protocol server for DeepSeek's advanced language models

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "dmontgomery40-deepseek-mcp-server": {
      "url": "https://mcp.deepseek.example/mcp",
      "headers": {
        "DEEPSEEK_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

You can run the DeepSeek MCP Server as an MCP-compatible endpoint that connects DeepSeek’s language models to MCP clients like Claude Desktop. This server handles model selection, configuration, and multi-turn conversations, making it easy to integrate advanced reasoning and dynamic prompts into your applications while keeping API usage simple.

How to use

Set up the MCP server as a local stdio endpoint so your MCP client can launch and communicate with it directly. The server is configured to start via a command that runs the package locally and exposes a standard interface for model selection, configuration options, and conversational context.

How to install

Prerequisites: make sure you have Node.js and npm installed on your system.

Option 1: Install via Smithery to automatically configure for Claude Desktop.

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

Option 2: Manual installation you can run locally.

npm install -g deepseek-mcp-server

Additional configuration and usage notes

Configure Claude Desktop to load the MCP server by adding a dedicated MCP server entry to your claude_desktop_config.json. The configuration below runs the server as a local process and provides an API key via environment variables.

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

Security and keys

Protect your API key and limit access to your MCP client configuration. Store keys securely and rotate them as needed.

Model behavior and fallbacks

The server can automatically fall back to an alternate model if the primary one is unavailable. You can switch models mid-conversation by directing prompts to the desired model, for example by selecting the alternate model name in your prompt or configuration. General recommendations: use the primary fast model for most tasks and switch to the secondary model for more technical or complex queries when needed.

Resource discovery is available for available models and configurations, including custom model selection and various controls such as temperature, max tokens, top P, presence penalty, and frequency penalty to tailor responses.

Enhanced conversation features

The server supports multi-turn conversations with complete message history and preserved configuration settings. It manages context behind the scenes so you can focus on the interaction itself, enabling long-running dialogues, troubleshooting, and advanced planning across multiple turns.

Testing with MCP Inspector

You can verify local setup and explore available tools by building and running the inspector. Build the server, then run the inspector against the built server to inspect tools, test chat completions with parameters, and monitor performance.

npm run build

# Run the inspector against the built server
npx @modelcontextprotocol/inspector node ./build/index.js