Terminal MCP – A sandboxed terminal interface for LLMs and beyond

Hacker News

Hi HN — I built Terminal MCP, a utility that exposes terminal sessions over MCP, primarily to help LLMs interact with CLIs and TUIs during development and debugging. It’s also useful outside of AI: it provides a general way to run terminal workflows through a controlled interface, with better isolation and observability than a raw shell. Recent addition: Sandbox mode, based on Anthropic’s work on safe tool execution. It lets you run sessions with explicit controls over what the process can access — by default blocking common locations where credentials live, and optionally restricting filesystem and network access. Repo: https://github.com/elleryfamilia/terminal-mcp Feedback welcome.

Visit product View on Hacker NewsJan 28, 2026

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2points

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

90%90% 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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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: way · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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