Heimdall – Open Source Observability Platform for MCP servers and apps

Hacker News

Hi HN! My friend and I have been working on this project for the past few days. While building and deploying MCP servers, we found it hard to understand how they were actually being used by a model in practice. Even after deployment, it was often unclear whether the model was calling our MCP tools at all, or how it was interacting with them. When things failed, we could not easily tell whether the issue came from our function definitions, our documentation, or how the model was interpreting and calling the tools. So we built Heimdall to trace how models call and use an MCP server. It shows which functions are invoked, with what arguments, and in what order, so we can better understand model behavior and refine our MCP setup. This is a very early-stage project, and there is still a lot to do. Feedback is very welcome. Thanks for reading!

Visit product View on Hacker NewsJan 29, 2026

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2points

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Launch Intel predictions

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

95%95% 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 · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% 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

16%16% 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.

Incorrect prediction on native model

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