Bring Rag
to LlamaIndex
Create your Vinkius account to connect R2R to LlamaIndex and start using all 6 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the R2R MCP Server?
Connect your R2R (Rag to Riches) deployment to an AI agent, bringing your RAG infrastructure inside your chat interface. By linking this server, the AI can query its own constructed knowledge base on demand.
What you can do
- Vector Search — Perform semantic similarity queries across your document database to retrieve contextually relevant chunks of information.
- Execute RAG Queries — Use the 'rag_query' endpoint to have the R2R server directly summarize information based on vector data.
- Knowledge Management — Call the API to list ingested documents, read metadata attributes, and filter logical collections.
- Instance Health Monitoring — Quickly ping the connection using health checks to verify your system is responsive.
How it works
- Enable the server integration.
- Provide your active R2R Base URL and Auth Key (if applicable).
- Trigger RAG requests natively within your supported chat interfaces.
Who is this for?
- AI & ML Engineers — Query your vector instances locally without needing Postman or external scripts.
- Data Custodians — Quickly verify document ingestions and browse metadata directly inside the terminal.
- Backend Developers — Audit engine responses and fine-tune semantic retrieval limits easily.
Built-in capabilities (6)
Retrieves details for a specific document
Checks the health status of the R2R server
Lists all document collections
Lists all ingested documents in the R2R system
Executes a RAG (Retrieval-Augmented Generation) query
Performs a vector search across ingested documents
Why LlamaIndex?
LlamaIndex agents combine R2R tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
- —
Data-first architecture: LlamaIndex agents combine R2R tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain R2R tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query R2R, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what R2R tools were called, what data was returned, and how it influenced the final answer
R2R in LlamaIndex
Why run R2R with Vinkius?
The R2R connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 6 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect R2R using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
R2R and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect R2R to LlamaIndex through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
R2R for LlamaIndex
Every request between LlamaIndex and R2R is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
What URL should I use for the R2R API URL?
If you are running R2R locally via Docker, it's typically http://localhost:7272. If you are using SciPhi Cloud or have it deployed on your own infrastructure, provide the exact public or private endpoint.
Do I need an R2R API Key?
It depends on your deployment. Open deployments for local testing may not require a key. Production deployments or SciPhi Cloud environments require you to provide the generated key.
What is the difference between RAG and Search?
The search tool issues a standard vector similarity match—it returns relevant raw snippets from your database. The rag_query tool asks the R2R server to perform the search and compute an intelligent answer wrapping those snippets using an LLM.
Are document ingestions possible via chat?
No. This integration is designed for observational toolsets (listing documents, inspecting states, querying the index). Heavy ingestions of PDFs or websites should be handled through scripts or the dashboard.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query R2R tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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