Can I see the accuracy metrics for a specific ML run through my agent?

Yes. Use the `get_attributes` tool with your Project ID and Run ID. Your agent will retrieve the detailed telemetry logged during that execution, including accuracy, loss, and any custom attributes defined in your code.

How do I check which model versions are currently stable in my registry?

The `list_models` tool retrieves all packaged ML models within a project. Your agent will expose the promoted model versions, helping you distinguish between experimental runs and stable candidates ready for deployment.

Can my agent search through hundreds of past ML experimentation runs?

Absolutely. Use the `search_runs` tool with your Project ID. Your agent will query Neptune's tracking server to identify historical experiment state checkpoints, making it easy to locate specific training results across your entire research timeline.

Neptune.ai (ML Experiment Tracking) MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: Neptune.ai (ML Experiment Tracking)
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 Neptune.ai (ML Experiment Tracking) 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="Neptune.ai (ML Experiment Tracking) Assistant",
            instructions=(
                "You help users interact with Neptune.ai (ML Experiment Tracking). "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Neptune.ai (ML Experiment Tracking)"
        )
        print(result.final_output)

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 Neptune.ai (ML Experiment Tracking) MCP Server

Connect your Neptune.ai account to any AI agent and take full control of your machine learning experimentation, model versioning, and training telemetry through natural conversation.

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

What you can do

Experiment Orchestration — List all managed ML projects and retrieve detailed metadata configurations tracking active runs and workspace boundaries directly from your agent
Run Audit & Search — Discover specific training runs or historical experiment state checkpoints mapping deep ML parameter sets and performance bounds securely
Attribute Inspection — Extract detailed telemetry capturing the exact variables, accuracy metrics, and loss curves logged during specific execution checkpoints natively
Model Registry Management — List and retrieve trained tracking models promoted and logged explicitly, isolating stable versions from ephemeral experimentation runs
Organizational Visibility — Enumerate accessible workspaces and projects to understand your ML research footprint and documentation distribution natively
Credential Audit — Verify specific user identifies and availability details bound inherently against your active service account token securely
Metadata Retrieval — Deep-dive into specific Project or Run IDs to retrieve precise JSON representations and chronological experimentation insights instantly

The Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) to OpenAI Agents SDK via MCP

Follow these steps to integrate the Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking)

Why Use OpenAI Agents SDK with the Neptune.ai (ML Experiment Tracking) MCP Server

OpenAI Agents SDK provides unique advantages when paired with Neptune.ai (ML Experiment Tracking) 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

Neptune.ai (ML Experiment Tracking) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Neptune.ai (ML Experiment Tracking) MCP Server delivers measurable value.

Automated workflows: build agents that query Neptune.ai (ML Experiment Tracking), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Neptune.ai (ML Experiment Tracking), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Neptune.ai (ML Experiment Tracking) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Neptune.ai (ML Experiment Tracking) to resolve tickets, look up records, and update statuses without human intervention

Neptune.ai (ML Experiment Tracking) MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect Neptune.ai (ML Experiment Tracking) to OpenAI Agents SDK via MCP:

get_attributes

Get parameters mapped within an experiment runtime bounds

get_project

Get specific details for a targeted Neptune ML project

get_user

Get specific user credentials and availability details

list_models

List trained tracking models packaged natively within a project

list_projects

List accessible Neptune workspaces and projects

search_runs

Search explicitly tracked ML experimentation runs inside a project

Example Prompts for Neptune.ai (ML Experiment Tracking) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Neptune.ai (ML Experiment Tracking) immediately.

"List all training runs for the 'Customer-Churn' project"

"Show me the metrics for run ID 'churn-exp-123'"

"List all registered models in project 'Fraud-Detection'"

Troubleshooting Neptune.ai (ML Experiment Tracking) MCP Server with OpenAI Agents SDK

Common issues when connecting Neptune.ai (ML Experiment Tracking) 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.

Neptune.ai (ML Experiment Tracking) + OpenAI Agents SDK FAQ

Common questions about integrating Neptune.ai (ML Experiment Tracking) 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.