Can my agent list all experiments for a specific DVC project?

Yes. Use the 'list_experiments' tool. Provide the project ID, and the agent will iterate through the model runs, returning a detailed history of metrics and execution logs for that project.

How do I see my custom dashboard views via chat?

Use the 'list_views' tool to see all your dashboard layouts. You can then use 'get_view' with a specific ID to retrieve the structural configuration and settings for that exact UI representation.

Can I audit my DVC Studio project settings through the agent?

Absolutely. The 'list_projects' and 'get_project' tools allow your agent to analyze identifier boundaries and repository metadata, helping you verify project connections and team mappings natively.

DVC MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: DVC
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect DVC through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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 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="DVC Assistant",
            instructions=(
                "You help users interact with DVC. "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from DVC"
        )
        print(result.final_output)

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 DVC MCP Server

Connect your DVC Studio account to any AI agent and take full control of your machine learning experiments and data versioning workflows through natural conversation.

The OpenAI Agents SDK auto-discovers all 6 tools from DVC through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries DVC, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Project Orchestration — Expose registered organization workspaces and validate available physical repositories connected within DVC Studio limits
Experiment Navigation — Iterate through explicitly generated model runs mapping precise metric arrays and discovering logged metrics history cleanly
View Management — Extract explicit UI configuration layouts and dashboard settings to retrieve structural workspace representations natively
Repository Auditing — Analyze specific identifier boundaries resolving internal team mappings and parsing direct repository metadata constraints
Metric Inspection — Retrieve complex structural arrays defining precisely which metrics were captured during specific experiment epochs
Identity Oversight — Identify the exact authorized token holder exposing mapping roles and organization scopes dynamically to verify permissions

The DVC MCP Server exposes 6 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 DVC to OpenAI Agents SDK via MCP

Follow these steps to integrate the DVC 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 6 tools from DVC

Why Use OpenAI Agents SDK with the DVC MCP Server

OpenAI Agents SDK provides unique advantages when paired with DVC 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

DVC + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the DVC MCP Server delivers measurable value.

Automated workflows: build agents that query DVC, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries DVC, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through DVC tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query DVC to resolve tickets, look up records, and update statuses without human intervention

DVC MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect DVC to OpenAI Agents SDK via MCP:

get_project

Get project

get_user

Get user profile

get_view

Get view

list_experiments

List experiments

list_projects

List projects

list_views

List views

Example Prompts for DVC in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with DVC immediately.

"List all projects in my DVC Studio account"

"Show me the last 5 experiments for project 'Credit-Scoring-Model'"

"What are my dashboard views in DVC?"

Troubleshooting DVC MCP Server with OpenAI Agents SDK

Common issues when connecting DVC to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

DVC + OpenAI Agents SDK FAQ

Common questions about integrating DVC MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.