https://github.com/tizee/mcp-server-deepseek
A MCP server provides access to DeepSeek-R1's reasoning capabilities for LLMs
https://github.com/tizee/mcp-server-deepseek
Last synced: 29 days ago
JSON representation
A MCP server provides access to DeepSeek-R1's reasoning capabilities for LLMs
- Host: GitHub
- URL: https://github.com/tizee/mcp-server-deepseek
- Owner: tizee
- License: mit
- Created: 2025-03-10T14:09:40.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-10T17:35:19.000Z (8 months ago)
- Last Synced: 2025-04-09T11:13:56.398Z (7 months ago)
- Language: Python
- Size: 20.5 KB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mcp-server-deepseek
A [Model Context Protocol (MCP)](https://modelcontextprotocol.io) 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](https://aider.chat/2025/01/24/r1-sonnet.html)
## 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 `` 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:
```bash
git clone https://github.com/yourusername/mcp-server-deepseek.git
cd mcp-server-deepseek
```
2. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install the package:
```bash
pip install -e .
```
4. Create a `.env` file with your DeepSeek API credentials:
```bash
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:
```bash
mcp-server-deepseek
```
Or use the development mode with the MCP Inspector:
```bash
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 `` 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:
```bash
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:
```bash
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](https://modelcontextprotocol.io) framework