Dataiku DSS MCP Server

A comprehensive Model Context Protocol (MCP) server for Dataiku DSS integration. This project provides Claude Code with direct access to Dataiku DSS for managing recipes, datasets, and scenarios.

🚀 Quick Start

Prerequisites

• Node.js 18.0.0+
• Dataiku DSS instance with API access
• Valid DSS API key

Installation

# Install globally
npm install -g @zhangzichao2008/mcp-dataiku

# Or use with npx
npx @zhangzichao2008/mcp-dataiku

Configuration

1. Copy environment template:

cp .env.sample .env

2. Configure your DSS connection in .env:

DSS_HOST=https://your-dss-instance.com:10000
DSS_API_KEY=your-api-key-here
DSS_INSECURE_TLS=true  # Only if using self-signed certificates

Claude Code Integration

Register the MCP server with Claude Code:

claude mcp add dataiku-dss \
    -e DSS_HOST=https://your-dss-instance.com:10000 \
    -e DSS_API_KEY=your-api-key-here \
    -e DSS_INSECURE_TLS=true \
    -- npx @zhangzichao2008/mcp-dataiku

📚 MCP Tool Catalog

Core Recipe Management Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | create_recipe | Create new recipe | project_key, recipe_type, recipe_name, inputs, outputs, code | | update_recipe | Update existing recipe | project_key, recipe_name, **kwargs | | delete_recipe | Delete recipe | project_key, recipe_name | | run_recipe | Execute recipe | project_key, recipe_name, build_mode |

Core Dataset Management Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | create_dataset | Create new dataset | project_key, dataset_name, dataset_type, params | | update_dataset | Update dataset settings | project_key, dataset_name, **kwargs | | delete_dataset | Delete dataset | project_key, dataset_name, drop_data | | build_dataset | Build dataset | project_key, dataset_name, mode, partition | | inspect_dataset_schema | Get dataset schema | project_key, dataset_name | | check_dataset_metrics | Get dataset metrics | project_key, dataset_name |

Core Scenario Management Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | create_scenario | Create new scenario | project_key, scenario_name, scenario_type, definition | | update_scenario | Update scenario settings | project_key, scenario_id, **kwargs | | delete_scenario | Delete scenario | project_key, scenario_id | | run_scenario | Execute scenario | project_key, scenario_id |

🔧 Advanced Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | get_scenario_logs | Get detailed run logs and error messages | project_key, scenario_id, run_id | | get_recipe_code | Extract actual Python/SQL code from recipes | project_key, recipe_name | | get_project_flow | Get complete data flow/pipeline structure | project_key | | get_dataset_sample | Get sample data from datasets | project_key, dataset_name, rows, columns | | get_recent_runs | Get recent run history across scenarios/recipes | project_key, limit, status_filter | | list_projects | List all available Dataiku projects | - |

Additional Dataset Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | list_datasets | List all datasets in a project | project_key, dataset_type (optional) | | get_dataset_info | Get detailed information about a dataset | project_key, dataset_name | | clear_dataset | Clear data from a dataset | project_key, dataset_name, partition (optional) |

Additional Recipe Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | list_recipes | List all recipes in a project | project_key, recipe_type (optional) | | get_recipe_info | Get detailed information about a recipe | project_key, recipe_name | | validate_recipe_syntax | Validate Python/SQL syntax of a recipe | project_key, recipe_name, code (optional) | | test_recipe_dry_run | Test recipe logic without actual execution | project_key, recipe_name, sample_rows |

Additional Scenario Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | list_scenarios | List all scenarios in a project | project_key, scenario_type, active_only | | get_scenario_info | Get detailed information about a scenario | project_key, scenario_id | | add_scenario_trigger | Add a trigger to a scenario | project_key, scenario_id, trigger_type, trigger params | | remove_scenario_trigger | Remove a trigger from a scenario | project_key, scenario_id, trigger_idx | | get_scenario_run_history | Get run history for a scenario | project_key, scenario_id, limit | | get_scenario_steps | Get step configuration including Python code | project_key, scenario_id | | clone_scenario | Clone an existing scenario with modifications | project_key, source_scenario_id, new_scenario_name, modifications |

Advanced Tools

| Tool | Description | Key Parameters | |------|-------------|----------------| | search_project_objects | Search for datasets, recipes, scenarios by name/pattern | project_key, search_term, object_types | | get_code_environments | List available Python/R environments | project_key (optional) | | get_project_variables | Get project-level variables and configuration | project_key | | get_connections | List available data connections | project_key (optional) | | get_job_details | Get detailed job execution information | project_key, job_id | | cancel_running_jobs | Cancel running jobs/scenarios | project_key, job_ids | | batch_update_objects | Update multiple objects with similar changes | project_key, object_type, pattern, updates | | get_project_flow | Get complete data flow/pipeline structure | project_key | | export_project_config | Export project configuration as JSON/YAML | project_key, format | | duplicate_project_structure | Copy project structure to new project | source_project_key, target_project_key, include_data |

Total: 46 Tools

🔧 Usage Examples

Core Operations

Creating a Python Recipe

{
  "project_key": "ANALYTICS_PROJECT",
  "recipe_type": "python",
  "recipe_name": "data_cleaner",
  "inputs": ["raw_data"],
  "outputs": [{"name": "clean_data", "new": true, "connection": "filesystem_managed"}],
  "code": "import pandas as pd\ndf = dataiku.Dataset(\"raw_data\").get_dataframe()\ndf_clean = df.dropna()\ndataiku.Dataset(\"clean_data\").write_with_schema(df_clean)"
}

Building a Dataset

{
  "project_key": "BI",
  "dataset_name": "user_analytics",
  "mode": "RECURSIVE_BUILD"
}

Getting Dataset Sample

{
  "project_key": "FINANCE_PROJECT",
  "dataset_name": "transactions",
  "rows": 500,
  "columns": ["customer_id", "amount"]
}

Getting Scenario Logs

{
  "project_key": "ANALYTICS_PROJECT",
  "scenario_id": "data_processing"
}

Exploring Project Structure

{
  "project_key": "SALES_ANALYTICS"
}

🏗️ Architecture

mcp-dataiku/
├── src/
│   ├── dataiku-client.ts   # Dataiku API client
│   └── mcp-server.ts       # MCP server implementation
├── package.json
├── tsconfig.json
├── .env.sample
└── README.md

🔒 Security

• API Key Protection: Store API keys in environment variables, never in code
• SSL Configuration: Support for self-signed certificates with DSS_INSECURE_TLS=true
• Permission Validation: All operations respect DSS user permissions
• Error Handling: Sensitive information is not exposed in error messages

📈 Monitoring

The MCP server provides logging for monitoring:

# Check logs for debugging
tail -f dataiku_mcp.log

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

Development Setup

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

# Run basic validation tests (no actual API calls)
npm test

# Run comprehensive tests (requires Dataiku DSS)
node test-comprehensive.js

# Clean build artifacts
npm run clean

# Publish new version (patch version)
npm run publish:patch

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• GitHub Repository
• npm Package
• Model Context Protocol
• Claude Code
• Dataiku DSS

📞 Support

If you encounter any issues or have questions, please open an issue on GitHub.

Ready to enhance your Dataiku workflows with AI assistance! 🚀