Neptune.ai (ML Experiment Tracking) Connector for AI agents.
6 live capabilities
Manage machine learning experiments and track training telemetry with your AI client.
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Why people use Neptune.ai (ML Experiment Tracking)
Neptune.ai for MLOps Experiment Tracking
This Connector lets you skip the navigation entirely. You can just ask your agent to pull the attributes for a specific run or list all your projects. You get the data you need in the chat window, which means you can stay in your flow and get back to the actual science.
What Vinkius changes
You get direct, conversational access to your entire ML experiment history without leaving your AI client.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Comparing accuracy across churn experiments
A data scientist needs to find the best run for a churn project.
- Real-world use case 02
Auditing the production model registry
An ML engineer wants to see what models are ready for production.
- Real-world use case 03
Retrieving hyperparameter logs
A researcher needs to check the learning rate of a failed run.
Complete set · 6capabilities
The complete Neptune.ai (ML Experiment Tracking) capability set.
These are the exact actions your AI can choose when you ask it to work with Neptune.ai (ML Experiment Tracking).
01—03
3 capabilities in this set.
Part of 6 available through Neptune.ai (ML Experiment Tracking).
- 01 Capability
Get attributes
Get parameters mapped within an experiment runtime bounds. Use this to see exact variables like learning rates.
- 02 Capability
List projects
List all accessible Neptune workspaces and projects. Use this to get a high-level overview of your research footprint.
- 03 Capability
Get project
Get specific details for a targeted Neptune ML project. This pulls precise metadata for a single workspace.
04—06
3 capabilities in this set.
Part of 6 available through Neptune.ai (ML Experiment Tracking).
- 04 Capability
Search runs
Search explicitly tracked ML experimentation runs inside a project. It helps you find historical checkpoints quickly.
- 05 Capability
Get user
Get specific user credentials and availability details. Use this to verify identities bound to your service account.
- 06 Capability
List models
List trained tracking models packaged natively within a project. This helps you isolate stable versions from test runs.
Set up in minutes
One URL. Then ask Neptune.ai (ML Experiment Tracking) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Neptune.ai (ML Experiment Tracking) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Neptune.ai (ML Experiment Tracking), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Neptune.ai (ML Experiment Tracking) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Neptune.ai (ML Experiment Tracking) URL.
- Step 03
Save and start
Save the connection and enable Neptune.ai (ML Experiment Tracking) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"neptuneai-ml-experiment-tracking": {
"url": "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Neptune.ai (ML Experiment Tracking)
Open Agent mode in chat and ask: "Using Neptune.ai (ML Experiment Tracking), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"neptuneai-ml-experiment-tracking": {
"url": "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Neptune.ai (ML Experiment Tracking)
Ask Copilot: "Using Neptune.ai (ML Experiment Tracking), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"neptuneai-ml-experiment-tracking": {
"url": "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Neptune.ai (ML Experiment Tracking)
Open Cascade and ask: "Using Neptune.ai (ML Experiment Tracking), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"neptuneai-ml-experiment-tracking": {
"url": "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Neptune.ai (ML Experiment Tracking)
Ask Cline: "Using Neptune.ai (ML Experiment Tracking), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add neptuneai-ml-experiment-tracking --transport http "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Neptune.ai (ML Experiment Tracking)
Ask Claude: "Using Neptune.ai (ML Experiment Tracking), show me...". 6 tools are ready
Where the request belongs
Work Neptune.ai can move forward.
This is for data scientists and ML engineers who are tired of manual dashboard navigation. It's for the person who needs to audit model performance or check training telemetry quickly during a sprint.
Data Scientist
Queries training history and compares accuracy metrics between different experimental runs on a Tuesday afternoon.
ML Engineer
Audits the model registry to ensure only stable versions are promoted to production.
AI Researcher
Tracks metadata across multiple projects to maintain consistent logging standards.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Arize AI
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Bring your own AI
Change the model, client or framework. Keep Neptune.ai connected.
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ChatGPT -
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Cursor -
VS Code -
Windsurf -
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Before you connect
Questions about Neptune.ai.
The practical details behind the request, access and result.
Can I use the Neptune.ai MCP to see my training metrics?
Yes. You can ask your agent to pull specific metrics like accuracy, loss curves, or any other telemetry logged during your runs.
How does the Neptune.ai MCP help with model versioning?
It allows you to list and view promoted models, making it easy to distinguish stable production versions from ephemeral test runs.
Can my AI agent list all my Neptune.ai projects?
Yes, your agent can pull a full list of your Neptune workspaces and projects to give you a high-level overview of your research footprint.
Does the Neptune.ai MCP support getting JSON metadata?
Yes. You can request precise JSON representations of specific projects or runs to get deep-dive details into your configurations.
How do I use Neptune.ai MCP to find specific experiment runs?
Just ask your agent to search for a run by name or criteria. It will scan your project history and return the specific checkpoints you need.
Can I check user permissions with the Neptune.ai MCP?
Yes, you can verify specific user credentials and availability bound to your active service account to ensure proper access.
Can I see the accuracy metrics for a specific ML run through my agent?
Yes. Use the get_attributes capability 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 capability 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 capability 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.
One connection away
Give your agent a direct line to Neptune.ai.
Connect Neptune.ai once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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