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I built an MCP server to connect AI agents to your DWH

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I built an MCP server to connect AI agents to your DWH

Hi all, this is Burak, I am one of the makers of Bruin CLI ( https://github.com/bruin-data/bruin ). We built an MCP server that allows you to connect your AI agents to your DWH/query engine and make them interact with your data. A bit of a back story: we started Bruin as an open-source CLI tool that brings together data ingestion, transformation, quality and governance. You can build data pipelines using SQL and Python, ingest data from many sources, run data quality checks and some more stuff, open-source. The goal has been to build a CLI experience that would make humans productive. After some time, agents popped up, and when we started using them heavily for our own development stuff, it became quite apparent that we might be able to offer similar capabilities for data engineering tasks. Agents can already use CLI tools, and they have the ability to run shell commands, which meant that they could technically use Bruin CLI as well. Our initial attempts were around building a simple `AGENTS.md` file with a set of instructions on how to use Bruin. It worked fine to a certain extent; however, it came with its own set of problems, primarily around maintenance. Every new feature/flag meant more docs to sync. It also meant the file needed to be distributed somehow to all the users, which would be a manual process. We then started looking into MCP servers: while they are great to expose remote capabilities, for a CLI tool, it meant that we would have to expose pretty much every command and subcommand we had as new tools. This meant a lot of maintenance work, a lot of duplication, and a large number of tools which bloat the context. Eventually, we landed on a middle-ground: expose only documentation navigation, not the commands themselves. In that spirit, we ended up with just 3 tools: - `bruin_get_overview` - `bruin_get_docs_tree` - `bruin_get_doc_content` The agent uses MCP to fetch docs, understand capabilities, and figure out the correct CLI invocation. Then it just runs the actual Bruin CLI in the shell. This means less manual work for us, and making the new features in the CLI automatically available to everyone else. You can now use Bruin CLI to connect your AI agents, such as Cursor, Claude Code, Codex, or any other agent that supports MCP servers, into your DWH. Given that all of your DWH metadata is in Bruin, your agent will automatically know about all the business metadata necessary. Here's a quick video of me demoing the tool: https://www.youtube.com/watch?v=604wuKeTP6U All of this is fully open-source, and you can run it anywhere. Bruin MCP works out of the box with: - BigQuery - Snowflake - Databricks - Athena - Clickhouse - Synapse - Redshift - Postgres - DuckDB - MySQL I would love to hear your thoughts and feedback on it, thanks! https://github.com/bruin-data/bruin

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, clickhouse · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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