Hopsworks MCP Server

MCP server for Hopsworks integration, providing a straightforward interface for LLMs to interact with Hopsworks.

Capabilities

Platform & Authentication

• Authentication - Connect to Hopsworks instances

• Projects - Create and manage Hopsworks projects

• Datasets - Handle file operations on Hopsworks

• Python Environments - Manage Python environments and dependencies

• Secrets - Securely store and retrieve sensitive information

Feature Store

• Feature Store - Interact with feature stores and run SQL queries

• Feature Groups - Manage feature groups and their data

• External Feature Groups - Connect to external data sources as feature groups

• Features - Work with individual features and their metadata

• Feature Views - Create and use feature views for model training and serving

• Expectations - Create and manage data validation rules

• Embeddings - Manage vector embeddings and similarity search

• Queries - Join, filter, and analyze feature data

• Spine Groups - Create and use spine groups for training data generation

• Training Datasets - Create and manage datasets for model training

• Transformation Functions - Create and manage feature transformation functions (one-to-one, one-to-many, many-to-one, many-to-many) with support for statistics-based transformations

Model Lifecycle

• Model Registry - Create, save, retrieve and manage ML models (TensorFlow, PyTorch, scikit-learn, Python, LLM)

• Model Serving - Deploy, manage and monitor ML models in production with advanced features like transformers, inference logging and batching

Jobs & Processing

• Jobs - Create and schedule jobs

• Executions - Run and monitor job executions

• Flink Clusters - Manage Flink clusters and jobs

Integrations

• Git Integration - Work with Git repositories within Hopsworks

• Kafka - Create and manage Kafka topics and schemas

• OpenSearch - Work with OpenSearch indexes

Related MCP server: Databricks MCP Server

Installation

pip install -e .

Development

# Install development dependencies
pip install -e ".[dev]"

# Run the server
fastmcp run main.py

# Use the interactive development environment
fastmcp dev main.py

Usage with Claude or other LLMs

Running the Server

You can run the Hopsworks MCP server in several ways:

# Run the server directly
python main.py

# Run using FastMCP
fastmcp run main.py

# Use the interactive development environment
fastmcp dev main.py

# Install in Claude Desktop for persistent access
fastmcp install main.py --name "Hopsworks Tools"

Configuring with Claude

To use the Hopsworks MCP server with Claude, you need to add it to Claude's configuration. The configuration file is typically located at:

• macOS: ~/Library/Application Support/Claude Desktop/config.json

• Windows: %APPDATA%\Claude Desktop\config.json

• Linux: ~/.config/Claude Desktop/config.json

Add the following configuration to your Claude settings:

{
  "mcpServers": {
    "hopsworks": {
      "command": "/path/to/your/python",
      "args": [
        "/path/to/mcp-hopsworks/main.py"
      ],
      "env": {
        "PYTHONPATH": "/path/to/mcp-hopsworks",
        "HOPSWORKS_API_KEY": "your_api_key_here",
        "HOPSWORKS_HOST": "your_hopsworks_host_url"
      }
    }
  }
}

Replace the placeholders with your specific paths and credentials:

• /path/to/your/python: The full path to your Python executable (e.g., /usr/bin/python3 or /Users/username/miniconda3/bin/python)

• /path/to/mcp-hopsworks: The full path to your mcp-hopsworks directory

• your_api_key_here: Your Hopsworks API key

• your_hopsworks_host_url: Your Hopsworks instance URL (e.g., "https://your-instance.hopsworks.ai")

Troubleshooting Connection Issues

If Claude has trouble connecting to the Hopsworks MCP server:

1. Python Path: Ensure you're using the absolute path to the Python executable that has the required packages installed:

   # Find your Python path
   which python3
   # Or
   python3 -c "import sys; print(sys.executable)"

2. Environment Variables: Make sure all required environment variables are set:

   • HOPSWORKS_API_KEY: Required for authentication with Hopsworks

   • HOPSWORKS_HOST: The URL of your Hopsworks instance

   • PYTHONPATH: Should include the path to the mcp-hopsworks directory

3. Required Packages: Verify that all required packages are installed:

   pip install -e .

4. Python Version: Ensure you're using Python 3.10 or higher:

   python --version

After updating your configuration, restart Claude completely for the changes to take effect.

Requirements

• Python 3.10+

• Hopsworks API access (API key with recommended scopes: featurestore, project, job, kafka)

Best Practices

Installation

• The Hopsworks Python client is installed with the Python profile (hopsworks[python]) to ensure all necessary dependencies are available for pure Python environments.

• For Spark environments, additional configuration may be required.

API Key

• When generating an API key, include the following scopes: featurestore, project, job, and kafka for full functionality.

• Store API keys securely and never commit them to version control.

Engine Selection

• Use the appropriate engine based on your environment:

  • python: For pure Python environments (default)

  • spark: For Apache Spark environments

  • hive: For Hive query execution

Version Compatibility

• The major and minor version of the Hopsworks Python library should match those of your Hopsworks deployment.

• Check your Hopsworks version in the Project's settings tab.

Transformation Functions

• Creating transformation functions:

  • One-to-one: Transform a single feature into a single output feature

  • One-to-many: Transform a single feature into multiple output features

  • Many-to-one: Combine multiple features into a single output feature

  • Many-to-many: Transform multiple input features into multiple output features

• Execution modes:

  • default: Uses Pandas UDF for batch operations, Python UDF for online inference

  • python: Always uses Python UDF regardless of operation type

  • pandas: Always uses Pandas UDF regardless of operation type

• Use statistics-based transformations for feature normalization and scaling

• Use context variables to share common parameters across multiple transformations

• Use the drop_features parameter to exclude input features from the output