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.

R2R MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: R2R
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add R2R as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to R2R. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in R2R?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About 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.

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.

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.

The R2R MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect R2R to LlamaIndex via MCP

Follow these steps to integrate the R2R MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from R2R

Why Use LlamaIndex with the R2R MCP Server

LlamaIndex provides unique advantages when paired with R2R through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine R2R tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain R2R tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query R2R, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what R2R tools were called, what data was returned, and how it influenced the final answer

R2R + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the R2R MCP Server delivers measurable value.

Hybrid search: combine R2R real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query R2R to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying R2R for fresh data

Analytical workflows: chain R2R queries with LlamaIndex's data connectors to build multi-source analytical reports

R2R MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect R2R to LlamaIndex via MCP:

get_document

Retrieves details for a specific document

get_health

Checks the health status of the R2R server

list_collections

Lists all document collections

list_documents

Lists all ingested documents in the R2R system

rag_query

Executes a RAG (Retrieval-Augmented Generation) query

search

Performs a vector search across ingested documents

Example Prompts for R2R in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with R2R immediately.

"Perform a vector search for 'Company Holiday Policy 2026'."

"Query the RAG engine to summarize known advanced RAG chunking strategies."

"Verify the operational health of the R2R server."

Troubleshooting R2R MCP Server with LlamaIndex

Common issues when connecting R2R to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

R2R + LlamaIndex FAQ

Common questions about integrating R2R MCP Server with LlamaIndex.

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.