Dataiku MCP

io.github.clssck由 GitHub 账号 clssck 发布v1.1.1更新于 Oct 27, 2026

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

已验证STDIO仅桌面开发者工具数据与分析

概览

AI 生成的概览

本地 MCP 服务器,让助手通过 Dataiku DSS REST API 查看并操作项目、流程、数据集、配方、作业、场景和文件夹。

可以做什么
  • 列出、读取并生成项目流程地图,含配方子类型与连接关系
  • 列出、查看、预览、下载、创建、更新和删除数据集与配方
  • 列出、查看、构建、等待和终止作业,并读取作业日志
  • 列出、运行和管理场景,读取或设置项目变量
  • 浏览托管文件夹及其内容,上传、下载和删除文件
  • 推断连接信息,列出代码环境
适用场景
适用于在 Dataiku DSS 中工作、希望助手梳理项目流程、查看数据集结构与预览、运行或监控构建与场景、管理文件夹和变量而无需切换到 DSS 界面的场景。
运行要求
以 Node.js 20+ 的 npm 包(dataiku-mcp)通过 stdio 在本机运行。需要 DATAIKU_URL 提供 DSS 实例地址、DATAIKU_API_KEY 提供 DSS API 密钥;DATAIKU_PROJECT_KEY 为可选的默认项目。需能访问该 DSS 实例。
安装前请注意
API 密钥可访问你的 DSS 实例,只应放在客户端的环境配置中。多个工具会写入或删除数据:数据集、配方和场景的创建/更新/删除,作业构建与终止,变量设置,文件夹上传与删除文件。README 说明该服务器已不再积极维护,并指向后续的 SDK。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Dataiku MCP,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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Dataiku MCP Server is no longer actively maintained.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

来源:README.md,提交 1d20e26

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v1.1.1最新Sep 16, 2026

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