What dependencies are required to run MCP RAG?
The system requires `uv` for package and environment management, plus API keys for Anthropic, Slack, and Brave Search.
@kira1228
Basic information
AI-powered Chat System with multiple MCP servers.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCP-RAG": {
"command": "uv",
"args": [
"venv"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP RAG is an AI-powered chat system built on the Model Context Protocol (MCP) client-server architecture. It connects to multiple specialized MCP servers—Filesystem, Slack, and Brave Search—to give Claude access to local files, Slack conversations, and real-time web search. This system is for users who want a single interface that automatically retrieves context from diverse sources without manual server selection.
First, install uv (see README for commands). Then clone the repository, create a .env file with your ANTHROPIC_API_KEY, SLACK_BOT_TOKEN, SLACK_TEAM_ID, and BRAVE_API_KEY. Create a virtual environment and install dependencies using uv venv and uv sync. Finally, run the client with uv run client.py path/to/dir/you/want/to/use.
uv.The system requires `uv` for package and environment management, plus API keys for Anthropic, Slack, and Brave Search.
Create a `.env` file in the project root and add `ANTHROPIC_API_KEY`, `SLACK_BOT_TOKEN`, `SLACK_TEAM_ID`, and `BRAVE_API_KEY` with your credentials.
MCP RAG uses three MCP servers: Filesystem (local folders), Slack (workspace conversations), and Brave Search (web). The system intelligently chooses which to query.
Yes, it is licensed under the MIT License, allowing free use, modification, and distribution.
Claude analyzes the question and automatically selects the appropriate MCP server (Filesystem, Slack, or Brave Search) without requiring explicit user instructions.
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.

MCP connector for Snipara, the Project Brain for AI coding agents

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,

Personal context for every AI: search, read, and write back to your private Markdown library of articles, threads, PDFs, notes, and captured ChatGPT/Claude/Gemini/Grok conversations. Remote server, OAuth 2.1 paste-and-au
Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.
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