Integrate Dataiku MCP server into your Slack workspace for instant access to your AI agent.
Tools that your AI agent can use through this MCP server to interact with Dataiku
Start a run of a DSS scenario — the usual way to kick off an orchestrated pipeline (a sequence of builds, checks and reporters) as opposed to building a single dataset, which Build Dataset does. Use List Scenarios to find a valid scenario ID. A successful call only means the run was accepted, and the response carries no run identifier, so poll List Scenario Runs to follow the outcome. Requires the RUN_JOBS privilege on the project. See the documentation
List the scenarios of a DSS project, with each scenario's id, whether it is currently running, and whether it is active (i.e. responding to its own triggers). Call this before Run Scenario to find a valid scenario ID, or to check whether a scenario is already in flight before starting another run. Requires the MONITOR_JOBS privilege on the project. See the documentation
Retrieve the last runs of a DSS scenario. Use this after Run Scenario, which returns no run identifier of its own, to follow the outcome: each entry carries a runId, start/end timestamps and a result object reporting outcome (e.g. SUCCESS) and type (e.g. SCENARIO_DONE). This tool cannot tell you which entry corresponds to a run you started: the response is just the scenario's recent runs, so a concurrent run, or one already in progress before you called Run Scenario, looks no different. Correlate deliberately — match on the start timestamp (the runId, e.g. 2016-04-15-16-57-37-759, is derived from it) against the moment you triggered the run, rather than assuming any particular entry is yours. Stop polling once the run you are tracking reports a result, whatever its outcome. Requires the RUN_JOBS privilege on the project. See the documentation
List the projects on the DSS instance. Start here when you only know a project by its display name: every other Dataiku tool is addressed by projectKey (e.g. MYPROJECT), which this tool returns. Only projects the connected API key holds the READ_CONF privilege on are listed, so an empty result usually means a permissions gap rather than an empty instance. Note that the free edition of DSS does not include Public API access — the API key must come from a trial or licensed instance. See the documentation
Retrieve the latest build jobs of a DSS project, each with its jobId and state. Use this to check what a project has been building recently, or to recover a jobId you no longer have before calling Get Job Status. Requires the READ_CONF privilege on the project. See the documentation
List the datasets of a DSS project. Use this to discover a dataset's name — the identifier Build Dataset needs to build it — along with its type (e.g. Filesystem) and connection parameters. Use List Projects first if you do not know the project key. Requires the READ_CONF privilege on the project. See the documentation
Check the status of a DSS build job, returned as baseStatus.status. Poll this after Build Dataset using the id it returned. NOT_STARTED and RUNNING mean the job is still in flight; DONE, FAILED and ABORTED are terminal, so stop polling on any of them and treat FAILED/ABORTED as an unsuccessful build. Use List Jobs to recover a job ID you no longer have. Requires the MONITOR_JOBS privilege on the project. See the documentation
Start a job that builds one or more outputs (typically datasets) in a DSS project. Use this to rebuild specific outputs directly; use Run Scenario instead when the pipeline is already orchestrated as a scenario. Use List Datasets to find valid output names. A successful call only means the job was accepted — the response's id is the job ID, which you pass to Get Job Status to follow it to completion. Requires the RUN_JOBS privilege on the project. See the documentation