Dataiku MCP

[!IMPORTANT]

## Sunset — use clssck/dataiku-sdk instead

Dataiku MCP Server is no longer actively maintained. Development has moved to the

[Dataiku DSS SDK](https://github.com/clssck/dataiku-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).

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

npm ci

npm run build

Run as a local CLI after build:

node dist/index.js

Use directly from npm (after publish):

npx -y dataiku-mcp

Local Build And Testing

Recommended local workflow from repo root:

# install deps

npm ci

static checks

npm run check

unit tests

npm test

build distribution

npm run build

run MCP server locally (dev)

npm start

Optional live DSS integration tests:

# requires DATAIKU_URL, DATAIKU_API_KEY, DATAIKU_PROJECT_KEY in .env

npm 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:

cp .env.example .env

then edit .env

Run directly in dev:

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):

{

"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:

{

"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:

{

"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:

{

"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:

{

"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:

{

"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

1. 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

• MCP local server connection docs: https://modelcontextprotocol.io/docs/develop/connect-local-servers
• Cursor MCP docs: https://cursor.com/docs/context/mcp
• Cline MCP docs: https://docs.cline.bot/mcp/configuring-mcp-servers