How do I secure an App Token for Ragas?

Log into your provided Ragas dashboard. In your project's settings or dedicated security section, you will find the ability to generate a new Application Token. Copy it immediately, as it may only appear once.

What format is required to upload a dataset?

The tool uses common array formats through the MCP wrapper. When passing data, the AI maps arrays containing `question`, `ground_truth` and `contexts` natively matching Ragas base requirements.

Does the server evaluate prompts automatically during testing?

Yes. When triggering evaluations, Ragas uses its own sophisticated metrics (like Faithfulness, Answer Relevance) running internally. The MCP server simply pipes these generated reports back to your chat.

Ragas MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Ragas
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Ragas 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 Ragas "
            "(7 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Ragas MCP Server

Integrate Ragas with your AI agent to bring professional grade RAG (Retrieval-Augmented Generation) evaluation and tracking into your chat interface. By subscribing to this server, the AI can seamlessly manage datasets and measure LLM performance on demand.

Pydantic AI validates every Ragas tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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

Dataset Management — Upload, list, and organize evaluation datasets directly inside your environment.
Run Evaluations — Automatically trigger Ragas evaluations on your RAG pipelines and fetch detailed scoring.
Track Experiments — Monitor and compare iterative improvements by viewing tracked metrics across different agent versions.
Project Organization — Associate evaluations with specific projects within your Ragas dashboard.

The Ragas MCP Server exposes 7 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 Ragas to Pydantic AI via MCP

Follow these steps to integrate the Ragas 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 7 tools from Ragas with type-safe schemas

Why Use Pydantic AI with the Ragas MCP Server

Pydantic AI provides unique advantages when paired with Ragas 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 Ragas integration code

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

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

Ragas + Pydantic AI Use Cases

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

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

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

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

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

Ragas MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Ragas to Pydantic AI via MCP:

get_dataset

Retrieves details for a specific evaluation dataset

get_experiment

Retrieves detailed information for a specific experiment

get_results

Retrieves the results of a completed experiment

list_datasets

Lists available evaluation datasets

list_experiments

Lists experiments associated with a specific dataset

list_metrics

Lists all available evaluation metrics

run_evaluation

g., faithfulness, answer_relevancy). Triggers a new evaluation run for a dataset

Example Prompts for Ragas in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Ragas immediately.

"List all Ragas datasets available in my project."

"Fetch the metrics and results for the recent experiment 'Support Bot V3'."

"Create a new Ragas project named 'Financial_RAG_Testing'."

Troubleshooting Ragas MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Ragas + Pydantic AI FAQ

Common questions about integrating Ragas 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 Ragas MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.