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R2R MCP Server

Name: R2R
Availability: InStock
Author: Vinkius

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Equip your AI with direct access to your R2R engine — execute vector searches, run precise RAG queries, and manage your documents.

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What is the R2R MCP Server?

The R2R MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to R2R via 6 tools. Equip your AI with direct access to your R2R engine — execute vector searches, run precise RAG queries, and manage your documents. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.

Built-in capabilities (6)

get_documentget_healthlist_collectionslist_documentsrag_querysearch

Tools for your AI Agents to operate R2R

Ask your AI agent "Perform a vector search for 'Company Holiday Policy 2026'." and get the answer without opening a single dashboard. With 6 tools connected to real R2R data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.

Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.

Why teams choose Vinkius

One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.

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R2R MCP Server capabilities

6 tools

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

Performs a vector search across ingested documents

What the R2R MCP Server unlocks

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

1. Enable the server integration.
2. Provide your active R2R Base URL and Auth Key (if applicable).
3. 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.

Frequently asked questions about the R2R MCP Server

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.