Ragas MCP Server for OpenAI Agents SDK 7 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Ragas through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Ragas Assistant",
instructions=(
"You help users interact with Ragas. "
"You have access to 7 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Ragas"
)
print(result.final_output)
asyncio.run(main())
* 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.
The OpenAI Agents SDK auto-discovers all 7 tools from Ragas through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Ragas, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Ragas MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 7 tools from Ragas
Why Use OpenAI Agents SDK with the Ragas MCP Server
OpenAI Agents SDK provides unique advantages when paired with Ragas through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Ragas + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Ragas MCP Server delivers measurable value.
Automated workflows: build agents that query Ragas, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Ragas, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Ragas tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Ragas to resolve tickets, look up records, and update statuses without human intervention
Ragas MCP Tools for OpenAI Agents SDK (7)
These 7 tools become available when you connect Ragas to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Ragas to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Ragas + OpenAI Agents SDK FAQ
Common questions about integrating Ragas MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Ragas with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Ragas to OpenAI Agents SDK
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.