Dataiku ACTION
Run Scenario
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- Action
- Writes data
- API key
- SDK
- MCP
IMPLEMENTATION
Call this tool
Connect a user's Dataiku account once, then configure and run Run Scenario from your backend or agent.
import { PipedreamClient } from "@pipedream/sdk"
const pd = new PipedreamClient({
projectId: process.env.PIPEDREAM_PROJECT_ID!,
clientId: process.env.PIPEDREAM_CLIENT_ID!,
clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
projectEnvironment: "production",
})
const result = await pd.actions.run({
id: "dataiku-run-scenario",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
dataiku: { authProvisionId: "apn_xxxxxxx" },
projectKey: "Project Key",
scenarioId: "Scenario ID",
},
})
console.log(result)curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
-H "Content-Type: application/json" \
-H "X-PD-Environment: production" \
-H "Authorization: Bearer {access_token}" \
-d '{
"external_user_id": "{external_user_id}",
"id": "dataiku-run-scenario",
"configured_props": {
"dataiku": { "authProvisionId": "apn_xxxxxxx" },
"projectKey": "Project Key",
"scenarioId": "Scenario ID"
}
}'// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
"x-pd-app-slug": "dataiku",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
name: "dataiku-run-scenario",
arguments: {
projectKey: "Project Key",
scenarioId: "Scenario ID",
},
})SCHEMA
Inputs
Pipedream supplies the connected account. Your application provides the operation-specific values below. Dynamic inputs are resolved against that user's account.
| Property | Type | Description |
|---|---|---|
projectKey Project Key | string | The key of the DSS project, e.g. MYPROJECT. Call List Projects and pass the projectKey field of the project you want. This is an identifier, not the project's display name — a display name will not resolve. In the Dataiku DSS GUI the same value appears in the project's URL as /projects/MYPROJECT/. Required |
scenarioId Scenario ID | string | The ID of the scenario within the project, e.g. STEPS_SCENARIO. Call List Scenarios and pass the id field of the scenario you want — a scenario also has a separate name (its display label), which this prop does not accept. Required |
triggerParams Trigger Parameters | object | Parameters passed to the scenario run, readable inside the scenario as trigger parameters. Example: {"triggerParam1": "value1", "triggerParam2": 49}. Omit if the scenario takes no parameters. Optional |
REFERENCE
Tool details
Behavior hints are published with the component in the Pipedream registry and surface as MCP tool annotations, so an agent can reason about a tool before it calls it.
- Registry key
- dataiku-run-scenario
- Version
- 0.0.2
- App
- Dataiku
- Authentication
- API key
- Read-only
- No
- Destructive
- No
- Open world
- Yes
- Source
- View on GitHub ↗