@tizee
基本信息
A MCP server provides access to DeepSeek-R1's reasoning capabilities for LLMs
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-deepseek-tizee": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
mcp-server-deepseek is 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. It acts as a bridge between LLM applications and DeepSeek’s reasoning model.
Install with Python 3.13+ and a DeepSeek API key: clone the repo, create a virtual environment, run pip install -e ., and set environment variables in a .env file. Start the server with mcp-server-deepseek (or make dev for development mode). The server exposes a single tool think_with_deepseek_r1 that accepts a prompt string and returns DeepSeek-R1’s reasoning wrapped in <thinking> tags.
<thinking> formatClone the repository, create a Python 3.13+ virtual environment, install with `pip install -e .`, copy `.env.example` to `.env`, and set your DeepSeek API key, model name, and API base URL (default `https://api.deepseek.com`).
It exposes a single tool `think_with_deepseek_r1` that takes a `prompt` (string) and returns DeepSeek-R1’s reasoning content enclosed in `<thinking>` tags.
Common issues include an incorrect DeepSeek API key (check your `.env` file), timeout errors on complex prompts (try simplifying the prompt), and missing reasoning content (try rephrasing the query). Detailed error logs are stored at `~/.cache/mcp-server-deepseek/server.log` and the log level can be set via the `LOG_LEVEL` environment variable.
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