bintocher

bintocher / mcp-superset

Public

MCP server for managing Apache Superset — 128+ tools for dashboards, charts, datasets, SQL Lab, access control

ai-tools apache-superset claude fastmcp llm
17
2
100% credibility
Found Mar 11, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A server that connects AI assistants to Apache Superset for full management of dashboards, charts, data, users, and security through simple commands.

How It Works

1
🔍 Discover the helper

You learn about a friendly tool that lets smart AI assistants manage your business charts and reports effortlessly.

2
📦 Set it up quickly

With one easy click, you install the helper on your computer.

3
🔗 Connect your dashboards

Share simple login details to link the helper to your existing dashboard service securely.

4
🤖 Invite your AI assistant

Point your favorite AI chat buddy, like Claude, to this new helper so they can team up.

5
✨ Command with words

Just tell your AI what to do – create charts, organize users, run data checks – and watch it happen safely.

🎉 Everything runs smoothly

Your dashboards stay perfect, access is controlled, and you save tons of time with AI doing the work.

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AI-Generated Review

What is mcp-superset?

mcp-superset is a Python MCP server for Apache Superset that hands AI assistants like Claude or GitHub Copilot full control over your BI instance. Developers get 128+ tools to manage dashboards, charts, datasets, SQL Lab queries, users, roles, RLS rules, and exports via natural language prompts. Install from PyPI with `pip install mcp-superset`, configure via .env (Superset URL/creds), and run `mcp-superset --transport stdio` for Cursor/VSCode or HTTP for web clients.

Why is it gaining traction?

It crushes alternatives like superset-mcp (60 tools) with triple the coverage, plus unique safety nets like confirmation flags for deletes, DDL blocking in SQL Lab, and auto-sync for datasource permissions. Flexible transports (HTTP/SSE/stdio) plug into mcp github copilot vscode, Claude Desktop, or n8n workflows, while CLI flags (`--host --port --env-file`) and uvx support make mcp server docker or mcp server python setups painless. Export/import ZIPs and permissions audits save hours on migrations.

Who should use this?

Superset admins tired of manual dashboard tweaks via UI, data teams building RLS/groups programmatically, or BI devs scripting charts/SQL in mcp github copilot or Cursor. Ideal for mcp server ai automation in project manager flows or mcp github issues triage.

Verdict

Grab it if you're on Superset 6.x and want AI-driven BI ops—docs are thorough, MIT-licensed, with PyPI/registry ready. 14 stars and 1.0% credibility flag its newness (low test coverage), but safety features and tool depth make it production-viable for early adopters over sparse rivals.

(198 words)

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