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.
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
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
Clone the repository:
git clone https://github.com/yourusername/mcp-server-deepseek.git
cd mcp-server-deepseek
Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Install the package:
pip install -e .
Create a .env file with your DeepSeek API credentials:
cp .env.example .env
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
