MCP that adds RAG for private repos

Hacker News

I know there’s a bunch of these tools already out there for public sources of info. I built this tool because I was getting frustrated by having to clone repos of libraries/APIs I'm using to be able to add them as context to the Cursor IDE (so that Cursor could use the most recent patterns). I would've preferred to just proxy GitHub search, but that seems to be limited to public repos via Copilot chat, and not available via graphql. This repo hosts a remote MCP server to facilitate a RAG query against an indexed GitHub repo. Indexing is performed via Cloudflare workflows, I’ve included info for how to deploy this in your own instance as I think having remotely accessible embeddings can be very useful for an organization. [readme]( https://github.com/edelauna/github-semantic-search-mcp/tree/... )

Visit product View on Hacker NewsJul 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, mcp, context · Missing: mac, agents, macos

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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

63%63% 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 · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

21%21% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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