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I built a deterministic scanner that catches command injection, prompt-injection markers in tool descriptions, over-broad/destructive tools, and committed secrets in MCP servers before you connect them. It continuously scans the entire official MCP registry and makes results available at https://index.canopii.dev . Now the engine behind that scanner is available as a CLI that you can integrate into your processes and CI/CD pipelines (e.g --min-grade B). If you are building MCP servers, you can make sure your MCP server is secure using the local mode before publishing it. Or check why your server scored low, apply fixes, check if your score has improved locally first before publishing a new version so as to eliminate guesswork. If you are a user, easily check the security of an MCP server before connecting your AI agents using one of the available methods (remote, github, pypi, npm etc.) You can also use an LLM as a "judge". It won't affect the score but it may flag important security considerations that a deterministic scanner can't catch. No telemetry, no account necessary. Github: https://github.com/canopii-dev/canopii-cli (license: Apache 2.0)

Visit product View on Hacker NewsJul 29, 2026

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

78%78% 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.

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

43%43% 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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% 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

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

Correct prediction on native model

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