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MCP server that teaches LLMs to write production grade Postgres SQL

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MCP server that teaches LLMs to write production grade Postgres SQL

For the past 10 years at Timescale (now Tiger Data), I've been been watching developers make mistakes in database design: forgetting to index foreign keys, creating case-sensitive email lookups, using arbitrary VARCHAR limits. And I've seen the pain and frustration when there is major downtime and botched migrations fixing things. AI coding could have made this better but is only making things worse. It's infuriating. So, I (along with my awesome team at Tiger Data) built an MCP server that auto-injects Postgres best practices into AI context to help. The biggest innovation: our get_prompt_template tool lets the LLM auto-discover the right guidance without slash commands. Ask "design a schema for IoT devices" and it automatically pulls our Postgres schema design and hypertable guides. What's different from other MCP servers: Instead of thin API wrappers, I am teaching AI things like "Always index foreign key columns - Postgres won't do it for you" and "use TEXT not VARCHAR(255)". It includes versioned Postgres docs (15-17) with hybrid search, plus TimescaleDB-specific patterns. Try it with our free service: ``` curl -fsSL https://cli.tigerdata.com | sh tiger auth login tiger mcp install ``` Works with Claude Desktop, Cursor, Windsurf, VS Code. Open source. By the way: this approach might show how to implement Anthropic's new Agent Skills (previous discussion: https://news.ycombinator.com/item?id=45607117 ) entirely through MCP.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos
97%97% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
33%33% 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
18%18% 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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