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DataFrey – MCP server for Snowflake with text-to-SQL agent

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DataFrey – MCP server for Snowflake with text-to-SQL agent

I’m a data scientist and I find it hard to use Claude Code for SQL - it doesn’t have DB context. so I made yet another database MCP server! only Snowflake support for now. I had to reconnect with nature after reading native Snowflake MCP setup docs so for my server I’ve made a nice CLI wizard to set up DB connection and install the Claude Code plugin: MCP + skill - you can ask it like `/db write dbt model to rank leads`. It also has a `plan` tool for complex questions. when you ask a blurry question, it triggers a separate text-to-SQL agent that uses 1. (kinda) RAG for your schema (along with some values) that builds during DB connection (if you agree) 2. subagents to explore your data. 3. planning. This is what Snowflake Cortex is supposed to do, but when I try it, it never finds the right tables. Database-as-MCP sounds like a security nightmare, but I put a lot of effort into making it safer. I’d appreciate any thoughts on the secure design. by default, CLI asks for select permissions on all schemas, not just information_schema. I’m convinced that it’s impossible to write good SQL without peeking into the data. maybe it's a hot take - share your thoughts! Everything is free and hosted by me, but rate-limited. In the future, I want to charge for planning calls above the limit. I have a bunch of ideas on how to make a smarter text-to-SQL, so I want to keep this part closed-source. I’ll open-source more though - it’s just deployed as a monolith now.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
92%92% 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 HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, calls · Missing: plus, platform, intuitive
26%26% 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 · Strong signals: smart · 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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