What are the prerequisites to run MCP-RAG?
You need to install `uv` and have Python available. API keys for Anthropic, Slack (bot token and team ID), and Brave Search are required.
@kira1228
基本信息
AI-powered Chat System with multiple MCP servers.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"MCP-RAG-": {
"command": "uv",
"args": [
"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-RAG is an AI-powered chat system that integrates multiple MCP servers (Filesystem, Slack, Brave-Search) to enable the Claude AI to retrieve and reference information from local documents, Slack conversations, and real-time web search based on user queries. It is built for users who want a unified assistant that can seamlessly access diverse data sources.
Install uv (see README for install commands), clone the repository, create a .env file with ANTHROPIC_API_KEY, SLACK_BOT_TOKEN, SLACK_TEAM_ID, and BRAVE_API_KEY, then set up a virtual environment and install dependencies with uv venv and uv sync. Run the client using uv run client.py path/to/dir/you/want/to/use.
You need to install `uv` and have Python available. API keys for Anthropic, Slack (bot token and team ID), and Brave Search are required.
Create a `.env` file and add `ANTHROPIC_API_KEY`, `SLACK_BOT_TOKEN`, `SLACK_TEAM_ID`, and `BRAVE_API_KEY` with your credentials.
MCP-RAG can access any folder specified in the `path/to/dir/you/want/to/use` argument when running the client.
The README does not specify data storage. The system accesses files, Slack, and web services temporarily during query processing.
No transport or authentication details beyond the API tokens are provided in the README.
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