Integration · MODEL CONTEXT PROTOCOL
oraichain ragflow mcp
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
连接能力
oraichain ragflow mcp 为 AI Agent 提供什么
来源目录未提供结构化功能列表，请在使用前阅读源仓库文档。
安全
MCP 收录不等于安全审核
MCP 服务器可能获得模型上下文、凭据、本地文件或调用外部系统的权限。连接 Agent 前，请检查代码、环境变量、网络行为、软件包来源与维护状态。
README
仓库文档
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
- Create a new conda environment:
conda create -n ragflow_mcp python=3.12
conda activate ragflow_mcp- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp- Install dependencies:
pip install -r requirements.txt- Install uv (A fast Python package installer and resolver):
curl -LsSf https://astral.sh/uv/install.sh | sh- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp- Create a new virtual environment and activate it:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate- Install dependencies:
uv pip install -r pyproject.toml-
Start the MCP server
-
Start the inspector using the following command:
# you can choose a different server
SERVER_PORT=9000 npx @modelcontextprotocol/inspector社区
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