R2R Connector for AI agents.
6 live capabilities
Connect your RAG infrastructure to your chat interface for instant knowledge retrieval.
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Why people use R2R
R2R : Stop manual data fetching for your RAG workflows
This Connector changes that by putting your RAG deployment right into your chat interface. Your agent can now look things up itself. When you ask a question, it doesn't just guess; it goes into your database, finds the relevant chunks, and pulls out the facts. You get to stop being the middleman.
What Vinkius changes
Your AI gets a direct line to your private knowledge base.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Finding specific policies
An engineer needs to find the remote work policy in a 500-page PDF.
- Real-world use case 02
Auditing document ingestion
A data manager wants to see if the latest HR docs were indexed.
- Real-world use case 03
Checking system uptime
A developer needs to know if the RAG engine is responding.
Complete set · 6capabilities
The complete R2R capability set.
These are the exact actions your AI can choose when you ask it to work with R2R.
01—03
3 capabilities in this set.
Part of 6 available through R2R.
- 01 Capability
Get document
Pull the specific details and metadata for a single document. Use this to inspect how your files are indexed.
- 02 Capability
List collections
View all the different document collections you've organized. This keeps your data structure visible to your agent.
- 03 Capability
Get health
Check if your R2R system is up and running correctly. It provides a quick way to verify your connection status.
04—06
3 capabilities in this set.
Part of 6 available through R2R.
- 04 Capability
Search
Run a semantic vector search to find relevant information in your data. It allows your agent to find specific context instantly.
- 05 Capability
Rag query
Execute a full RAG query to get summarized answers from your data. This gives your agent the ability to synthesize complex info.
- 06 Capability
List documents
See every file currently stored in your R2R system. This helps you quickly verify your data inventory.
Set up in minutes
One URL. Then ask R2R to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use R2R from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it R2R, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable R2R for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the R2R URL.
- Step 03
Save and start
Save the connection and enable R2R in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"r2r": {
"url": "https://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using R2R
Open Agent mode in chat and ask: "Using R2R, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"r2r": {
"url": "https://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using R2R
Ask Copilot: "Using R2R, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"r2r": {
"url": "https://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using R2R
Open Cascade and ask: "Using R2R, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"r2r": {
"url": "https://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using R2R
Ask Cline: "Using R2R, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add r2r --transport http "https://edge.vinkius.com/vk_preview_Psy9XiP4IhGY7SchHirciLtxL89aKlE6LAoJStd2/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using R2R
Ask Claude: "Using R2R, show me...". 6 tools are ready
Where the request belongs
Work R2R can move forward.
This is for the data engineers and researchers who need to query massive amounts of internal documentation without manually feeding files into every single prompt.
Data Custodian
Verifies that new documents are indexed correctly and browses metadata to ensure high-quality data ingestion.
ML Engineer
Tests vector search accuracy and tunes RAG retrieval limits directly through a chat interface.
Backend Developer
Audits engine responses and monitors system health to ensure the RAG infrastructure stays online.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsCognita (RAG Framework)
Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.
Verba
Connect your Verba RAG platform to your AI agent. Search your documents, retrieve semantic answers, and manage your Weaviate knowledge base directly.
Haystack (deepset Cloud)
Build and manage AI-powered search and RAG pipelines via deepset Cloud. search documents, run pipelines, and manage workspaces.
Amazon Bedrock KB
Connect your AI agent to AWS Bedrock Knowledge Bases. execute semantic searches, managed RAG, and sync vector datasources natively.
FastGPT
Manage FastGPT Knowledge Bases. automate dataset creation, document ingestion, and RAG search directly from any AI agent.
VectorShift (AI Workflow & RAG Automation)
Automate AI workflows and RAG via VectorShift. manage pipelines, query knowledge bases, and deploy chatbots directly from any AI agent.
Bring your own AI
Change the model, client or framework. Keep R2R connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about R2R.
The practical details behind the request, access and result.
How does the R2R MCP help with my private data?
It gives your AI a direct line to your private knowledge base. Instead of feeding files manually, your agent can query your RAG infrastructure to find facts on demand.
Can I use R2R MCP to search my company's PDFs?
Yes, it uses vector search to find specific sections of your PDFs. This means your agent can pull out the exact context it needs from your company's documents.
Do I need to set up a database first?
You need an active R2R deployment. This Connector connects to an existing RAG infrastructure, so you'll need your Base URL and Auth Key ready to go.
How does R2R MCP handle summaries?
It uses the RAG query capability to synthesize answers. Your agent will look at your vector data and provide a summary based on what's actually in your files.
Can I check my document status with R2R MCP?
Yes, you can list all ingested documents directly in the chat. This makes it easy to see what's been processed without checking a separate dashboard.
Will R2R MCP work with my existing RAG setup?
Yes, as long as your RAG system is compatible with the R2R protocol. You just need to provide the correct connection details in your settings.
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 capability issues a standard vector similarity match—it returns relevant raw snippets from your database. The rag_query capability 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.
One connection away
Give your agent a direct line to R2R.
Connect R2R once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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