alibabacloud-dataworks-mcp-server: A Model Context Protocol server that provides tools for AI, allowing it to interact with the DataWorks Open

by aliyun · MCP Server · ★ 50

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install alibabacloud-dataworks-mcp-server

npm install -g alibabacloud-dataworks-mcp-server
npm install alibabacloud-dataworks-mcp-server

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 60/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aialiyunbigdatadatadata-analysisdata-sciencedata-visualizationdatabaseidemodeling

Quick Facts

Stars50
Forks16
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score60/100
Open Issues11
Last Updated2026-06-16
Created2025-03-26
Platformsmcp, node
Est. Tokens~13k

About alibabacloud-dataworks-mcp-server

Try DataWorks Agent Want a ready-to-use AI experience for DataWorks without manual MCP setup? DataWorks Agent is Alibaba Cloud's built-in intelligent assistant for data development and operations. It connects to your DataWorks workspace out of the box, so you can use natural language to explore metadata, develop nodes, troubleshoot tasks, and manage resources—no local MCP server configuration...

Compatible Skills

These tools work well together with alibabacloud-dataworks-mcp-server for enhanced workflows:

  • ChatLab — semantic(0.33)+complementary+rare_topics+same_lang+shared_platform (60%)
  • datasets-for-start — semantic(0.46)+complementary+rare_topics+similar_pop (60%)
  • datastoria — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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