Rockfish MCP Server

# Rockfish MCP Server A Model Context Protocol (MCP) server that provides access to the Rockfish API, enabling AI assistants to interact with Rockfish's machine learning platform. ## Features This MCP server provides tools for the following Rockfish resources: - **Databases**: Create, list, update, and delete databases - **Worker Sets**: Manage worker sets for distributed processing - **Workflows**: Create and manage ML workflows - **Models**: Upload, list, and manage ML models - **Projects**: Organize and manage projects - **Datasets**: Create and manage datasets ## Installation 1. Clone the repository and set up your virtual environment (Python 3.12 or below): ```bash git clone https://github.com/yourusername/rockfish-mcp.git cd rockfish-mcp python3.11 -m venv .venv source .venv/bin/activate ``` 2. Install dependencies (choose one method): **Method A: Install with dev tools (recommended for contributors):** ```bash pip install -e ".[dev]" ``` **Method B: Install from requirements.txt (exact locked versions):** ```bash pip install -r requirements.txt ``` **Method C: Install runtime only (for production):** ```bash pip install -e . ``` 3. Set up environment variables: ```bash cp .env.example .env # Edit .env and add your Rockfish API key ``` ## Configuration Create a `.env` file with your Rockfish API credentials: ```env ROCKFISH_API_KEY=your_api_key_here ROCKFISH_API_URL=https://api.rockfish.ai ``` If you want to use a specific Rockfish Organization and/or Rockfish Project, add the following to the `.env` file too: ```env ROCKFISH_ORGANIZATION_ID=your_organization_id_here ROCKFISH_PROJECT_ID=your_project_id_here ``` ## Usage Run the MCP server: ```bash python -m rockfish_mcp.server ``` Or use the console script: ```bash rockfish-mcp ``` ## Claude Desktop Setup To use this MCP server with Claude Desktop: 1. **Complete some of the installation steps above** (clone, install dependencies). Note that you do not need to start the MCP server manually for using it with Claude Desktop. Claude Desktop will automatically start it for you when you follow the steps below. Also, the .env file doesn't need to be created, we will be adding the environment variables to the Claude setup. 2. **Find your Claude Desktop configuration directory:** - **macOS**: `~/Library/Application Support/Claude/` - **Windows**: `%APPDATA%\Claude\` - **Linux**: `~/.config/Claude/` 3. **Create or edit the `claude_desktop_config.json` file** in that directory: Note that setting `ROCKFISH_ORGANIZATION_ID` and `ROCKFISH_PROJECT_ID` is optional. If you don't set these variables, the default organization and/or default project will be used. ```json { "mcpServers": { "rockfish": { "command": "/path/to/your/project/.venv/bin/python", "args": ["-m", "rockfish_mcp.server"] } } } ``` **Note:** Environment variables (API keys, URLs, organization/project IDs) are configured in the `.env` file in the project root, not in `claude_desktop_config.json`. 4. **Update the paths in the configuration:** - Replace `/path/to/your/project/.venv/bin/python` with the actual path to your Python executable - All API keys and URLs should be configured in the `.env` file (see step 5) 5. **Get the correct Python path** by running this command in your project directory: ```bash which python ``` 6. **Example configuration** (replace with your actual path): ```json { "mcpServers": { "rockfish": { "command": "/Users/shane/code/rockfish-mcp/.venv/bin/python", "args": ["-m", "rockfish_mcp.server"] } } } ``` Configure your API key and URL in the `.env` file: ```bash ROCKFISH_API_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ROCKFISH_API_URL=https://api.rockfish.ai ``` 7. **Restart Claude Desktop** after making these changes 8. **Test the connection** by asking Claude to list your Rockfish databases or projects ## MCP Inspector Setup The MCP Inspector is a debugging tool that helps you test your MCP server before connecting it to Claude Desktop. ### Installation ```bash npx @modelcontextprotocol/inspector ``` ### Usage 1. **Start the MCP Inspector:** ```bash npx @modelcontextprotocol/inspector /Users/shane/code/rockfish-mcp/.venv/bin/python -m rockfish_mcp.server ``` 2. **Or create a test script** for easier repeated testing: ```bash #!/bin/bash # test-mcp.sh export ROCKFISH_API_KEY="your_api_key_here" export ROCKFISH_API_URL="https://api.rockfish.ai" npx @modelcontextprotocol/inspector /Users/shane/code/rockfish-mcp/.venv/bin/python -m rockfish_mcp.server ``` Make it executable and run: ```bash chmod +x test-mcp.sh ./test-mcp.sh ``` 3. **The Inspector will open in your browser** and show: - Available tools (should show all 32 Rockfish tools) - Tool schemas and descriptions - Interactive tool testing interface 4. **Test your tools** by: - Selecting a tool from the list (e.g., `list_databases`) - Filling in required parameters - Clicking "Call Tool" to test the API call - Viewing the response ### Useful Tools to Test First - **`list_databases`** - Simple GET request with no parameters - **`list_projects`** - Another simple list operation - **`get_database`** - Test with a database ID from the list - **`create_database`** - Test creating a new resource ### Troubleshooting - **MCP server not appearing**: Check that the Python path is correct and the virtual environment is activated - **Authentication errors**: Verify your `ROCKFISH_API_KEY` is correct - **Connection issues**: Confirm your `ROCKFISH_API_URL` is accessible - **Path issues on Windows**: Use forward slashes or escaped backslashes in JSON paths ## Available Tools ### Database Tools - `list_databases`: List all databases - `create_database`: Create a new database - `get_database`: Get a specific database by ID - `update_database`: Update a database - `delete_database`: Delete a database ### Worker Set Tools - `list_worker_sets`: List all worker sets - `create_worker_set`: Create a new worker set - `get_worker_set`: Get a specific worker set by ID - `delete_worker_set`: Delete a worker set - `get_worker_set_actions`: List actions that the specific worker set can run - `list_available_actions`: List all actions available to the user (across all worker sets) ### Workflow Tools - `list_workflows`: List all workflows - `create_workflow`: Create and run a new workflow - `get_workflow`: Get a specific workflow by ID - `update_workflow`: Update a workflow ### Model Tools - `list_models`: List all models - `upload_model`: Upload a new model - `get_model`: Get a specific model by ID - `delete_model`: Delete a model ### Organization Tools - `get_active_organization`: Get the currently active organization - `list_projects`: List all organizations ### Project Tools - `get_active_project`: Get the currently active project - `list_projects`: List all projects - `create_project`: Create a new project - `get_project`: Get a specific project by ID - `update_project`: Update a project ### Dataset Tools - `list_datasets`: List all datasets - `create_dataset`: Create a new dataset - `get_dataset`: Get a specific dataset by ID - `update_dataset`: Update a dataset - `delete_dataset`: Delete a dataset - `get_dataset_schema`: Get dataset metadata present in its schema ### SDK Tools (Synthetic Data Generation) These tools use the Rockfish Python SDK to provide end-to-end synthetic data generation workflows: **Configuration Management:** - `obtain_train_config`: Generate training configuration for TabGAN model with automatic column type detection - `update_train_config` [experimental]: Modify training hyperparameters or field classifications **Workflow Execution:** - `start_training_workflow`: Start TabGAN training workflow using cached configuration - `get_workflow_logs`: Stream workflow logs with configurable level (DEBUG/INFO/WARN/ERROR) and timeout - `get_trained_model_id`: Extract trained model ID from completed training workflow **Synthetic Data Generation:** - `start_generation_workflow`: Start generation workflow from trained model - `obtain_synthetic_dataset_id`: Extract generated dataset ID from completed workflow **Quality Assessment:** - `plot_distribution`: Generate distribution plots comparing real and synthetic data - `get_marginal_distribution_score`: Calculate similarity score between real and synthetic data ## Development ### Setup To contribute to this project, install with dev dependencies: ```bash pip install -e ".[dev]" ``` This installs black, isort, and pytest for code formatting and testing. ### Code Formatting This project uses **black** and **isort** to maintain consistent code style. **Format your code before committing:** ```bash # Format imports isort src/rockfish_mcp/ # Format code style black src/rockfish_mcp/ ``` **Check formatting without modifying files:** ```bash isort --check-only src/rockfish_mcp/ black --check src/rockfish_mcp/ ``` ### Running Tests The project includes automated tests for the Manta incident injection tools. To run tests, you'll need to set up the required environment variables: **Required Environment Variables:** ```bash # Add these to your .env file or export them ROCKFISH_API_KEY=your_api_key_here ROCKFISH_API_URL=https://api.rockfish.ai MANTA_API_URL=https://manta.rockfish.ai ROCKFISH_ORGANIZATION_ID=your_organization_id ROCKFISH_PROJECT_ID=your_project_id INCIDENT_CREATION_TEST_DATASET=your_test_dataset_id ``` **Running the tests:** ```bash # Run all tests pytest tests/ # Run specific test file pytest tests/test_create_incident_dataset.py # Run tests with verbose output pytest -v tests/ # Run tests and see print output pytest -s tests/ ``` **Test Coverage:** - **test_create_incident_dataset.py**: Tests the `create_incident_dataset` tool with all four incident types: - `instantaneous-spike-data`: Sudden spikes in time-series data - `sustained-magnitude-change-data`: Sustained changes over a time period - `data-outage-data`: Simulated data gaps/outages - `value-ramp-data`: Gradual ramping changes Test configurations are defined in [tests/incidents.yaml](tests/incidents.yaml) **Note:** The `INCIDENT_CREATION_TEST_DATASET` should be a valid dataset ID in your Rockfish project with time-series data suitable for incident injection testing. ### Contributing To contribute to this project: 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Format your code with isort and black 5. Add tests if applicable 6. Submit a pull request ## License MIT License