Elite Obsidian RAG System - Second Brain with Claude Code Integration
Aegntic MCP becomes available to MCP clients through the aegntic mcp mcp server. Elite Obsidian RAG System - Second Brain with Claude Code Integration.
An elite Retrieval-Augmented Generation (RAG) system that transforms Obsidian vaults into AI-paired cognitive workflow engines with advanced Graphiti knowledge graph integration.
The server is distributed via PyPI as obsidian-elite-rag-mcp, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
Before the server will start you need to supply 2 environment variables: OPENAI_API_KEY, QDRANT_HOST. Keep credentials in your client's env block or a secrets manager rather than committing them.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Aegntic MCP sits in that group. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
{
"mcpServers": {
"aegntic": {
"command": "uvx",
"args": ["obsidian-elite-rag-mcp"],
"env": {
"OPENAI_API_KEY": "your-value",
"QDRANT_HOST": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| QDRANT_HOST | Endpoint or connection string the server talks to. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.