mcp-server-deepseek

A Model Context Protocol (MCP) server that provides access to DeepSeek-R1's reasoning capabilities, allowing non-reasoning models to generate better responses with enhanced thinking.

• read R1+Sonnet set SOTA on aider’s polyglot benchmark

Overview

This server acts as a bridge between LLM applications and DeepSeek's reasoning capabilities. It exposes DeepSeek-R1's reasoning content through an MCP tool, which can be used by any MCP-compatible client.

The server is particularly useful for:

• Enhancing responses from models without native reasoning capabilities

• Accessing DeepSeek-R1's thinking process for complex problem solving

• Adding structured reasoning to Claude or other LLMs that support MCP

Related MCP server: DeepSeek MCP Server

Features

• Access to DeepSeek-R1: Connects to DeepSeek's API to leverage their reasoning model

• Structured Thinking: Returns reasoning in a structured <thinking> format

• Integration with MCP: Fully compatible with the Model Context Protocol

• Error Handling: Robust error handling with detailed logging

Installation

Prerequisites

• Python 3.13 or higher

• An API key for DeepSeek

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/mcp-server-deepseek.git
   cd mcp-server-deepseek

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the package:

   pip install -e .

4. Create a .env file with your DeepSeek API credentials:

   cp .env.example .env

5. Edit the .env file with your API key and model details:

   MCP_SERVER_DEEPSEEK_MODEL_NAME=deepseek-reasoner
   MCP_SERVER_DEEPSEEK_API_KEY=your_api_key_here
   MCP_SERVER_DEEPSEEK_API_BASE_URL=https://api.deepseek.com

Usage

Running the Server

You can run the server directly:

mcp-server-deepseek

Or use the development mode with the MCP Inspector:

make dev

MCP Tool

The server exposes a single tool:

think_with_deepseek_r1

This tool sends a prompt to DeepSeek-R1 and returns its reasoning content.

Arguments:

• prompt (string): The full user prompt to process

Returns:

• String containing DeepSeek-R1's reasoning wrapped in <thinking> tags

Example Usage

When used with Claude or another LLM that supports MCP, you can trigger the thinking process by calling the tool:

Please use the think_with_deepseek_r1 tool with the following prompt:
"How can I optimize a neural network for time series forecasting?"

Development

Testing

For development and testing, use the MCP Inspector:

npx @modelcontextprotocol/inspector uv run mcp-server-deepseek

Logging

Logs are stored in ~/.cache/mcp-server-deepseek/server.log

The log level can be configured using the LOG_LEVEL environment variable (defaults to DEBUG).

Troubleshooting

Common Issues

• API Key Issues: Ensure your DeepSeek API key is correctly set in the .env file

• Timeout Errors: Complex prompts may cause timeouts. Try simplifying your prompt

• Missing Reasoning: Some queries might not generate reasoning content. Try rephrasing

Error Logs

Check the logs for detailed error messages:

cat ~/.cache/mcp-server-deepseek/server.log

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgements

• Thanks to the DeepSeek team for their powerful reasoning model

• Built with the Model Context Protocol framework