Which integrations are available for this server?

Provides tools to manage recipes, datasets, and scenarios in Dataiku DSS, including creating, updating, deleting, running, and inspecting various objects.

How do I use Dataiku DSS MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Dataiku DSS MCP Server list datasets in project 'Sales'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

Copy environment template:

cp .env.sample .env

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

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

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit changes: git commit -m 'Add amazing feature'
Push to branch: git push origin feature/amazing-feature
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

📞 Support

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

Ready to enhance your Dataiku workflows with AI assistance! 🚀

Dataiku DSS MCP Server

Dataiku DSS MCP Server

🚀 Quick Start

Prerequisites

Installation

Configuration

Claude Code Integration

📚 MCP Tool Catalog

Core Recipe Management Tools

Core Dataset Management Tools

Core Scenario Management Tools

🔧 Advanced Tools

Additional Dataset Tools

Additional Recipe Tools

Additional Scenario Tools

Advanced Tools

🔧 Usage Examples

Core Operations

Creating a Python Recipe

Building a Dataset

Getting Dataset Sample

Getting Scenario Logs

Exploring Project Structure

🏗️ Architecture

🔒 Security

📈 Monitoring

🤝 Contributing

Development Setup

📄 License

🔗 Links

📞 Support

Resources

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