Bring Rag
to Pydantic AI
Create your Vinkius account to connect R2R to Pydantic AI 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 Pydantic AI?
Pydantic AI validates every R2R tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your R2R integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your R2R connection logic from agent behavior for testable, maintainable code
R2R in Pydantic AI
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 Pydantic AI 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 Pydantic AI
Every request between Pydantic AI 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 Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your R2R MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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