Ragas MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Ragas as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Ragas. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Ragas?"
)
print(response)
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.
LlamaIndex agents combine Ragas tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Ragas MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Ragas MCP Server
LlamaIndex provides unique advantages when paired with Ragas through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Ragas tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Ragas tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Ragas, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Ragas tools were called, what data was returned, and how it influenced the final answer
Ragas + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Ragas MCP Server delivers measurable value.
Hybrid search: combine Ragas real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Ragas to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Ragas for fresh data
Analytical workflows: chain Ragas queries with LlamaIndex's data connectors to build multi-source analytical reports
Ragas MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Ragas to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Ragas to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRagas + LlamaIndex FAQ
Common questions about integrating Ragas MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.