Basedash
AI-native Business Intelligence Platform
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Max Musing

Basedash MCP server - Your data analyst, in every AI tool you already use

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Basedash
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Basedash is now an MCP server. Connect Claude, Cursor, ChatGPT, or any MCP-compatible client and your AI agent can ask Basedash anything about your data — across every database, warehouse, and SaaS tool you've already connected to your workspace. It can pull live numbers, compare cohorts, generate charts, and dig into trends, all governed by the same access controls your team already uses. Your data analyst, inside every tool you ship in.

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Max Musing
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Hey Product Hunt 👋 We're flipping the usual integration model: instead of plugging more tools *into* Basedash, this lets you plug Basedash *into* the AI tools you already use. Add the Basedash MCP server to Claude, Cursor, ChatGPT, Windsurf, or any MCP-compatible client, and that agent suddenly grows the ability to look up live numbers across your databases, warehouses, and SaaS tools — same data, same permissions, same answers your team already trusts inside Basedash. Some things we use it for, today: - A PM asking Claude *"how is revenue tracking this week?"* and getting an answer with a chart, not a meeting. - An engineer in Cursor asking the agent *"how many users actually use the CSV export?"* before deciding whether to optimise it. - A founder asking ChatGPT *"compare 30-day retention for Pro vs Free"* before writing a board update. It's the same data analyst you'd ask inside Basedash — now reachable from wherever you already work. Setup is one URL. Auth is OAuth. Permissions are inherited from your workspace. Free to try. Would love your feedback.
Phil Leggetter

Love @Basedash. The MCP could be a game changer for some of my Claude cowork workflows 🙌

Moh

@maxmusing flipping the integration direction is the right call. Every other BI tool asks agents to come to the data — this makes the data go to where the agent already is. The permissions-inheritance model is the part I'd bet on: agents querying live numbers inside the same access controls your team trusts removes the "shadow analytics" problem. How are you handling schema discovery for databases with hundreds of tables — does the MCP expose a search/filter layer or does the agent get the full schema dump?

U Sai Likhith

Flipping the model so Basedash plugs into your AI tools instead of the other way around is great because that's where the queries actually happen. I'm curious to know if the MCP server handle ambiguous questions where the answer depends on how a metric is defined, or if it surface the raw data and let the LLM interpret.

Laksh Arora

very fascinated by the fact that we can now integrate a BI model into any mcp- compatible tool. I am very interested to know more about the administrative side of things or more like if a company could control what rows/columns a user can access as there can be many attributes containing sensitive info that the company would not like for the users to see.

Sujal Gupta

The mcp server is the right call , bringing data to the agent instead of building another dashboard humans have to open. quick question: if i connect two separate postgres instances, can it join across both in a single prompt or does schema discovery stay siloed per connection?