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 Pydantic AI 6 tools — connect in under 2 minutes

Name: R2R
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect R2R through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to R2R "
            "(6 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

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.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the R2R MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the R2R MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your R2R integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your R2R connection logic from agent behavior for testable, maintainable code

R2R + Pydantic AI Use Cases

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

Type-safe data pipelines: query R2R with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple R2R tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query R2R and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock R2R responses and write comprehensive agent tests

R2R MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect R2R to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

R2R + Pydantic AI FAQ

Common questions about integrating R2R MCP Server with Pydantic AI.

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.