Dataiku MCP

io.github.clssckPublished by GitHub account clssckv1.1.1Updated Oct 27, 2026

MCP server for Dataiku DSS project, flow, and operations APIs.

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Overview

AI-generated overview

Local MCP server that lets an assistant inspect and operate Dataiku DSS projects, flows, datasets, recipes, jobs, scenarios and folders via the DSS REST API.

What you can do
  • List, read and map projects, including normalized flow maps with recipe subtypes and connectivity
  • List, inspect, preview, download, create, update and delete datasets and recipes
  • List, inspect, build, wait on and abort jobs, and read job logs
  • List, run and manage scenarios, and read or set project variables
  • Browse managed folders and their contents, upload and download files, delete files
  • Infer connection details and list code environments
When to use it
Useful when you work in Dataiku DSS and want an assistant to explain a project's flow, check dataset schemas or previews, run or monitor builds and scenarios, or manage folders and variables without switching to the DSS UI.
Requirements
Runs locally as a Node.js 20+ npm package (dataiku-mcp) over stdio. Needs the Dataiku DSS instance URL in DATAIKU_URL and a DSS API key in DATAIKU_API_KEY; DATAIKU_PROJECT_KEY is an optional default project. Network access to the DSS instance is required.
Before you install
The API key grants access to your DSS instance, so store it only in the client's environment config. Many tools write or delete: dataset, recipe and scenario create/update/delete, job build and abort, variable set, folder upload and delete_file. The README states the server is no longer actively maintained and points to a successor SDK.

Installation

In SourceWeft

  1. Open Dataiku MCP in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

[!IMPORTANT]

Sunset — use clssck/dataiku-sdk instead

Dataiku MCP Server is no longer actively maintained. Development has moved to the Dataiku DSS SDK — a schema-first TypeScript SDK and dss CLI that is more actively maintained and covers far more of the DSS API:

  • Broader coverage — 30+ DSS resources vs this server's narrow tool set.
  • A scriptable dss CLI with a machine-readable command contract for agents.
  • One-command agent skill install for Claude, Codex, Cursor, Pi, and OMP.

Migrate here → https://github.com/clssck/dataiku-sdk


Dataiku MCP Server

MCP server for Dataiku DSS REST APIs, focused on flow analysis and reliable day-to-day operations (projects, datasets, recipes, jobs, scenarios, folders, variables, connections, and code environments).

[Install MCP Server]

Cursor one-click install includes placeholder environment values. Update DATAIKU_URL, DATAIKU_API_KEY, and optionally DATAIKU_PROJECT_KEY after adding the server.

What You Get

  • Deterministic normalized flow maps (project.map) with recipe subtypes and connectivity.
  • Summary-first outputs with explicit raw/detail toggles where needed.
  • Broad test coverage (unit + live integration + optional destructive integration suite).
  • Strong error taxonomy in responses: not_found, forbidden, validation, transient, unknown with retry hints.

Tool Coverage

  • project: list, get, metadata, flow, map
  • dataset: list, get, schema, preview, metadata, download, create, update, delete
  • recipe: list, get, create, update, delete, download
  • job: list, get, log, build, buildAndWait, wait, abort
  • scenario: list, run, status, get, create, update, delete
  • managed_folder: list, get, contents, download, upload, delete_file
  • variable: get, set
  • connection: infer
  • code_env: list, get

Prerequisites

  • Node.js 20+
  • npm
  • Dataiku DSS URL + API key

Quick Start

bash
npm cinpm run build

Run as a local CLI after build:

bash
node dist/index.js

Use directly from npm (after publish):

bash
npx -y dataiku-mcp

Local Build And Testing

Recommended local workflow from repo root:

bash
# install depsnpm ci
# static checksnpm run check
# unit testsnpm test
# build distributionnpm run build
# run MCP server locally (dev)npm start

Optional live DSS integration tests:

bash
# requires DATAIKU_URL, DATAIKU_API_KEY, DATAIKU_PROJECT_KEY in .envnpm run test:integration
# includes destructive actions (create/update/delete)DATAIKU_MCP_DESTRUCTIVE_TESTS=1 npm run test:integration

Repository Layout

  • src/: MCP server and tool implementations.
  • tests/: unit + integration test suites.
  • examples/: demos, fixtures, artifacts, and ad-hoc local scripts.
  • bin/: package executable entrypoint.
  • dist/: compiled output (generated).

Create a local env file:

bash
cp .env.example .env# then edit .env

Run directly in dev:

bash
npm start

Example scripts and sample outputs are kept under examples/ to avoid root-level clutter.

Environment Variables

  • DATAIKU_URL: DSS base URL
  • DATAIKU_API_KEY: DSS API key
  • DATAIKU_PROJECT_KEY (optional): default project key
  • DATAIKU_REQUEST_TIMEOUT_MS (optional): per-attempt request timeout in milliseconds (default: 30000)
  • DATAIKU_RETRY_MAX_ATTEMPTS (optional): max attempts for retry-enabled requests (GET only, default: 4, cap: 10)
  • DATAIKU_DEBUG_LATENCY (optional): set to 1/true to include per-tool timing diagnostics in structuredContent.debug.latency (off by default)

MCP Client Setup Guide

Use this server command in clients (npm package):

json
{  "command": "npx",  "args": ["-y", "dataiku-mcp"],  "env": {    "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",    "DATAIKU_API_KEY": "your_api_key",    "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"  }}

Windows note: if your MCP client launches commands without a shell, use npx.cmd:

json
{  "command": "npx.cmd",  "args": ["-y", "dataiku-mcp"],  "env": {    "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",    "DATAIKU_API_KEY": "your_api_key",    "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"  }}

You can also run TypeScript directly during development:

json
{  "command": "npx",  "args": ["tsx", "/absolute/path/to/Dataiku_MCP/src/index.ts"],  "env": {    "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",    "DATAIKU_API_KEY": "your_api_key",    "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"  }}

Claude Desktop

  1. Open Claude Desktop -> Settings -> Developer -> Edit Config.
  2. Add this under mcpServers in claude_desktop_config.json:
json
{  "mcpServers": {    "dataiku": {      "command": "npx",      "args": ["-y", "dataiku-mcp"],      "env": {        "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",        "DATAIKU_API_KEY": "your_api_key",        "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"      }    }  }}

Cursor

Cursor supports both project-scoped and global MCP config:

  • Project: .cursor/mcp.json
  • Global: ~/.cursor/mcp.json

Example:

json
{  "mcpServers": {    "dataiku": {      "command": "npx",      "args": ["-y", "dataiku-mcp"],      "env": {        "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",        "DATAIKU_API_KEY": "your_api_key",        "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"      }    }  }}

Cline (VS Code extension)

  1. Open Cline -> MCP Servers -> Configure MCP Servers.
  2. Add this server block in cline_mcp_settings.json:
json
{  "mcpServers": {    "dataiku": {      "command": "npx",      "args": ["-y", "dataiku-mcp"],      "env": {        "DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",        "DATAIKU_API_KEY": "your_api_key",        "DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"      }    }  }}

Codex / project-level MCP config

This repo already includes a project-scoped MCP file at .mcp.json. The checked-in .mcp.json uses node node_modules/tsx/dist/cli.mjs src/index.ts for cross-platform startup (including Windows); run npm ci first.

NPM Release Workflow

This repo includes a manual GitHub Actions release workflow:

  • Workflow file: .github/workflows/release.yml
  • Trigger: Actions -> Release NPM Package -> Run workflow

Inputs:

  • bump: patch | minor | major
  • version: optional exact version (overrides bump)
  • publish: whether to publish to npm

Required repository configuration:

  • GitHub variable: NPM_RELEASE_ENABLED=true
  • Optional variable: NPM_PUBLISH_ACCESS=public
  • Trusted publisher configured on npmjs.com for this package/repo/workflow

The workflow will:

  1. Install dependencies, run checks/tests, and build.
  2. Bump package version and create git tag.
  3. Push commit + tag to main.
  4. Publish to npm with GitHub OIDC trusted publishing (if publish=true).
  5. Create a GitHub Release with generated notes.

Trusted publishing setup (npm):

  1. Open https://www.npmjs.com/package/dataiku-mcp -> Settings -> Trusted Publisher.
  2. Choose GitHub Actions.
  3. Set:
    • Organization or user: clssck
    • Repository: Dataiku_MCP
    • Workflow filename: release.yml
  4. Save.

Official MCP Registry

This repo is configured for MCP Registry publishing:

  • Metadata file: server.json
  • Workflow: .github/workflows/publish-mcp-registry.yml
  • Required package field: mcpName in package.json

Server namespace:

  • io.github.clssck/dataiku-mcp

Publish paths:

  1. Manual: run Publish to MCP Registry in GitHub Actions.
  2. Automatic: run the npm release workflow with publish=true (it triggers MCP Registry publish).

Validation notes:

  • server.json.name must match package.json.mcpName.
  • server.json.packages[].identifier + version must reference a real npm publish.

Recommended Verification Prompt

After adding the server in a client, run:

  • project with { "action": "map", "projectKey": "YOUR_PROJECT_KEY" } (defaults to maxNodes=300, maxEdges=600; override as needed)

You should receive a flow summary in text and normalized nodes, edges, stats, roots, and leaves under structuredContent.map. When truncation limits are applied (default maxNodes=300, maxEdges=600), structuredContent.truncation reports before/after node+edge counts and whether truncation occurred.

Notes

  • project.map returns a compact text summary; full normalized graph is in structuredContent.map.
  • Arrays in normalized map output are deterministically sorted to reduce diff churn.
  • job.wait and job.buildAndWait include structuredContent.normalizedState with one of terminalSuccess | terminalFailure | timeout | nonTerminal while preserving raw DSS state.
  • With DATAIKU_DEBUG_LATENCY=1, responses include per-tool and per-API-call latency metrics under structuredContent.debug.latency.
  • List-style responses are token-bounded by default; use limit/offset (and action-specific caps like maxNodes, maxEdges, maxKeys, maxPackages) to page or expand results when needed.
  • dataset.get and job.get are summary-first by default; pass includeDefinition=true to include full DSS JSON in structuredContent.definition.

Sources

Source: README.md at commit 1d20e26

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.1.1LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts