Neptune.ai (ML Experiment Tracking) MCP Server
Manage ML experiments via Neptune.ai — track training runs, monitor metrics, and audit model versions.
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What is the Neptune.ai MCP Server?
The Neptune.ai MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Neptune.ai via 6 tools. Manage ML experiments via Neptune.ai — track training runs, monitor metrics, and audit model versions. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate Neptune.ai
Ask your AI agent "List all training runs for the 'Customer-Churn' project" and get the answer without opening a single dashboard. With 6 tools connected to real Neptune.ai data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Neptune.ai (ML Experiment Tracking) MCP Server capabilities
6 toolsGet parameters mapped within an experiment runtime bounds
Get specific details for a targeted Neptune ML project
Get specific user credentials and availability details
List trained tracking models packaged natively within a project
List accessible Neptune workspaces and projects
Search explicitly tracked ML experimentation runs inside a project
What the Neptune.ai (ML Experiment Tracking) MCP Server unlocks
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.
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
How it works
1. Subscribe to this server
2. Enter your Neptune.ai API Token
3. Start managing your ML experiments from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Scientists — monitor training progress and verify model metrics through natural conversation without manual dashboard navigation
- ML Engineers — audit the model registry and verify experiment attributes directly from your workspace terminal
- AI Researchers — track production model versions and ensure consistent metadata logging across multiple ML projects efficiently
Frequently asked questions about the Neptune.ai (ML Experiment Tracking) MCP Server
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.
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Connect Neptune.ai (ML Experiment Tracking) 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.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
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Give your AI agents the power of Neptune.ai MCP Server
Production-grade Neptune.ai (ML Experiment Tracking) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.