Dataiku DSS MCP for AI Agents. Manage Enterprise Data Pipelines and Model Monitoring
The Dataiku DSS MCP connects your AI client directly to your entire data science environment. You can list projects, check dataset schemas, monitor complex pipeline jobs, and audit ML model performance without leaving your chat interface. It puts full control of enterprise data workflows right into conversation.
Give Claude and any AI agent real-world access
List all accessible DSS projects and retrieve detailed structural information about their datasets.
Get the column names, data types, and full structure for any specified dataset in a project.
Track build tasks, training runs, and job status by listing pipeline jobs and analyzing their current state or timing.
Retrieve the exact configuration structure for recipes—whether they use Python, SQL, or visual tools—to audit data flow.
List available automation scenarios and trigger their execution to securely rebuild pipelines or retrain models.
Identify saved machine learning models and retrieve detailed performance metrics, including the specific trained schema layers.
List all installed plugins and data source connections (like cloud storage or APIs) to verify organizational access rights.
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What AI agents can do with 14 Tools for Data Science Workflow Management
Use these tools to control every aspect of your DSS environment, from listing projects to triggering complex data transformations.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Dataiku DSS MCPList Projects
Lists all DSS projects that your API key has access to.
Get Project
Retrieves metadata, settings, and tags for a specific Dataiku project.
List Datasets
Lists every dataset contained within a specified project.
Dataset Schema
Provides the complete column names and data types for any given dataset schema.
List Recipes
Lists all defined recipes, which are your data transformation workflows, in a...
List Jobs
Shows all pipeline jobs associated with a project, covering build tasks and model training runs.
Get Job
Gets the current status, timing data, and outputs for a specific job run.
List Scenarios
Retrieves a list of available automation scenarios within a project.
List Models
Lists all machine learning models that have been saved or deployed in the project.
Get Model
Retrieves metadata, algorithm details, and performance metrics for a specific ML...
Run Scenario
Triggers an automation scenario execution, which can rebuild pipelines or retrain...
List Plugins
Lists all DSS plugins that have been installed in the environment.
List Connections
Shows a list of data connections, including configured databases, cloud storage accounts, or APIs.
Get Recipe
Retrieves the full configuration and settings for a specific data transformation...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with Dataiku DSS, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
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Dataiku DSS MCP: Auditing Data Pipelines and Recipes
Right now, auditing a data pipeline means jumping between the DSS UI, downloading logs, cross-referencing Python scripts, and manually comparing schema versions across multiple tabs. It's slow, error-prone detective work.
With this MCP, you simply ask your agent to audit the transformation logic. You can use `list_recipes` to see every workflow available and then drill down with `get_recipe` to pull the precise configuration structure into a readable chat format. The painful clicking stops; you get actionable data instantly.
Dataiku DSS MCP: Monitoring ML Model Performance
Previously, checking model health required navigating to the 'Models' tab, finding the specific deployment, and then running a separate performance report. It was a multi-step process just for a status update.
Now, you can ask your agent to list models (`list_models`) and immediately request detailed metrics using `get_model`. You get the algorithm used, the trained schema layers, and key performance indicators right in the chat—no dashboard navigation required.
What Dataiku DSS MCP for AI Agents MCP does for your AI
Need to manage collaborative data science work in a natural way? This MCP lets you talk to your Dataiku DSS instance like it’s an extension of your own brain. Instead of navigating dozens of tabs and clicking through build logs, you just ask your AI agent for what you need—whether that's listing all available projects or checking the precise schema of a raw dataset.
You get immediate status updates on pipeline jobs, monitor training runs, and even trigger automation scenarios to rebuild pipelines when something breaks. It’s full command-line control over data science workflows, accessed via natural language conversation. When you connect this MCP through Vinkius, your agent gets access to the entire catalog of tools needed to manage everything from model metadata to underlying data connections.
019d7582-315c-7179-a27e-efc75014bf8f How to set up Dataiku DSS MCP for AI Agents MCP
The bottom line is that your AI client acts as a single command center for every aspect of your Dataiku data science workflow.
Subscribe to this MCP and provide your Dataiku Instance URL along with a valid API key (Personal, Project, or Global).
Your AI agent connects to the service endpoint managed by Vinkius.
You then use natural conversation to execute data science commands, like asking the system to list all projects in an environment.
Who uses Dataiku DSS MCP for AI Agents MCP
This MCP is built for the core team running an enterprise data platform. If you’re a Data Scientist spending too much time clicking between tabs to check job status or verify data schemas, this saves your day. It's designed for people who need deep control over complex data pipelines and ML models.
You track pipeline jobs and use natural language to verify recipe configurations across different transformation types.
You trigger automation scenarios and monitor deployed models in real-time, ensuring continuous deployment integrity.
You check dataset schemas and compare model performance metrics without having to leave your primary research flow.
You audit project metadata and review data connections across the entire organization to maintain governance.
Benefits of connecting Dataiku DSS MCP for AI Agents MCP
Instead of manually checking job status, you simply ask your agent to list jobs and get the current execution state or timing.
You can audit data logic by asking for the explicit configuration structures of recipes (Python, SQL, Visual), verifying transformations instantly.
Triggering pipelines used to require CLI commands; now you just tell your agent to run a scenario, like rebuilding datasets or retraining models.
Model performance review is faster. You get detailed metrics and schema layers for saved ML models without needing to open the DSS UI.
System oversight gets simple. You can list all data connections and installed plugins to quickly verify organizational constraints.
You gain full visibility into your entire data graph, from listing projects to checking dataset schemas in one conversational flow.
Dataiku DSS MCP for AI Agents MCP use cases
Investigating why a model failed
The MLOps team notices the 'Fraud-Detection' model score dropped. They ask their agent to get the model details, check performance metrics, and then use dataset_schema on the source data to see if the input structure changed.
Verifying a complex ETL job
A Data Engineer needs to confirm that an old sales forecasting pipeline ran correctly. They list the jobs, check the status of the last run using get_job, and then use get_recipe on the transformation recipe to audit the exact SQL logic used.
Setting up a new environment
An Analytics Manager needs an overview. They list all projects available, check which plugins are installed via list_plugins, and verify if cloud storage connections are properly listed using list_connections.
Resuming interrupted data flow
A Data Scientist is working on a new segmentation project. They notice the build tasks failed due to bad source data. They use run_scenario to trigger the pipeline rebuild and then check dataset_schema to confirm the raw input columns are correct.
Dataiku DSS MCP for AI Agents MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating it like a simple file listing
Asking your agent to 'show me data for the project' will just list projects, but you won't get the schemas or job statuses.
To understand what's inside a dataset, always use the dataset_schema tool after running list_datasets. If you need status updates, start with list_jobs.
Forgetting context dependencies
Trying to check model performance without knowing which project or dataset it relates to. The agent will fail because the scope is missing.
Always use get_project first to confirm your current working scope, then proceed with listing models (list_models) and checking their metrics using get_model.
Over-relying on manual auditing
Manually comparing the recipe configuration against what is actually running in production. This takes hours of clicking.
Use list_recipes to see all available transformations, and then use get_recipe to pull the precise YAML/JSON structure directly into your chat for instant comparison.
When to use Dataiku DSS MCP for AI Agents MCP
Use this MCP if you need deep, programmatic control over data pipeline governance. You're managing complex, production-grade data science workflows and need an agent to monitor job states (get_job), audit recipes (get_recipe), or trigger critical automation steps (run_scenario). Don’t use it if your goal is simple data lookup; for that, a standard database query tool will be faster. If you just want to view documentation, look for a knowledge base MCP instead. This is for operational control: knowing what ran, why it failed, and how to fix it.
Frequently asked questions about Dataiku DSS MCP for AI Agents MCP
How do I check if my dataiku projects are connected to external databases? +
The MCP allows you to list all data connections and installed plugins. This lets you quickly audit your entire environment by seeing which cloud storage, APIs, or SQL databases are linked to your DSS instance.
Can this MCP help me monitor if a model is performing well? +
Yes, you can list saved machine learning models and then request detailed performance metrics. This helps data scientists compare schema layers and track changes in prediction quality directly through conversation.
Does Dataiku DSS MCP let me run manual pipeline jobs? +
Absolutely. You can use the tools to list all available pipeline jobs, check their status using get_job, and even trigger a full rebuild or retraining cycle via automation scenarios.
What if I need to audit the SQL logic in my data transformations? +
You can retrieve recipes by listing them first, then using the specific tool to pull the explicit configuration structure. This allows you to verify exact Python or SQL code without opening the DSS interface.
How do I find out what projects I have access to? +
You simply ask your agent to list all accessible DSS projects. It provides a comprehensive overview, including project metadata and tags, so you know exactly what resources are available for your team.