Neptune.ai (ML Experiment Tracking)

Neptune.ai (ML Experiment Tracking) MCP Connector for Claude

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Manage ML experiments via Neptune.ai — track training runs, monitor metrics, and audit model versions.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner

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
mlopsexperiment-trackingmodel-versioningtraining-metricsdata-sciencetelemetry

6 tools expose this connector's capabilities to your AI agent.

list_projects

List accessible Neptune workspaces and projects

get_project

Get specific details for a targeted Neptune ML project

search_runs

Search explicitly tracked ML experimentation runs inside a project

get_attributes

Get parameters mapped within an experiment runtime bounds

list_models

List trained tracking models packaged natively within a project

get_user

Get specific user credentials and availability details

See how to talk to your AI agent using Neptune.ai (ML Experiment Tracking).

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

I've retrieved the training history for 'Customer-Churn'. Highlights include 'churn-exp-123' (Accuracy: 0.92), 'churn-baseline' (Accuracy: 0.85), and 5 other recent runs. Would you like to see the loss curve for the top-performing run?

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

Retrieving attributes for 'churn-exp-123'… The model achieved a final accuracy of 92.4% with a validation loss of 0.15. Learning rate was set to 0.001. No anomalous gradients were detected during the final epochs. Would you like the full JSON of all 45 logged attributes?

List all registered models in project 'Fraud-Detection'

I've identified 3 promoted models in 'Fraud-Detection': 'XGBoost-Classifier-v2', 'RandomForest-Baseline', and 'NeuralNet-Prod-v1'. All models are mapped to production-ready weights. Which one would you like to inspect for version history?

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.

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