Compatible with every major AI agent and IDE
Get dataset on Ragas
Retrieves details for a specific evaluation dataset
Get experiment on Ragas
Retrieves detailed information for a specific experiment
Get results on Ragas
Retrieves the results of a completed experiment
List datasets on Ragas
Lists available evaluation datasets
List experiments on Ragas
Lists experiments associated with a specific dataset
List metrics on Ragas
Lists all available evaluation metrics
Run evaluation on Ragas
g., faithfulness, answer_relevancy). Triggers a new evaluation run for a dataset
How Vinkius protects your data
Can I set different limits for each virtual assistant on my team?
Absolutely. You have full control in our command center. You can create an AI agent that only "reads" data so the support team can answer questions, and another superpowered agent that can "edit" and "create" information exclusively for your operations team. Each AI gets exactly the level of access you allow.
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.
How does the AI access my passwords and credentials?
It simply doesn't. On Vinkius, your passwords, API keys, and login details are kept in a secure vault. The AI (like ChatGPT or Claude) merely "asks" Vinkius to perform the task. Vinkius opens the door, does the work, and hands the result back to the AI. Your credentials are never seen, read, or learned by the artificial intelligence.
What happens if the underlying API rate limits my agent?
Our edge infrastructure automatically handles backoffs, queueing, and throttling. If an AI agent sends too many erratic requests, Vinkius manages the rate limits gracefully, ensuring your backend doesn't crash.
Supported Use Cases for Ragas
Build automated workflows with Cursor and Claude Code by connecting to the Ragas MCP server.
Autonomous rag Strategies
The Ragas connection gives ChatGPT direct access to rag tools. The integration handles the logic required for continuous ai frontier operations.
Scaling llm evaluation via MCP
Add llm evaluation functionality to your custom chatbots. The Ragas MCP handles the payload formatting required for ChatGPT and Claude to interface with ai frontier endpoints.
Ragas. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
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.
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