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CLI-use – turn any MCP server into a CLI in one command

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CLI-use – turn any MCP server into a CLI in one command

Hi everyone, I built cli-use, a small Python tool that turns any MCP server into a native CLI. The idea is simple: HTTP has curl, Docker has docker, Kubernetes has kubectl — MCP should have a shell-native client too. Why I made it: MCP is useful, but using it through agents has overhead: every session pays schema discovery cost every call carries JSON-RPC framing responses are often verbose JSON when the useful output is just a line or two cli-use converts that into a terse CLI so tools can be called like normal shell commands. Example: pip install cli-use cli-use add fs /tmp cli-use fs list_directory --path /tmp After that, the MCP server behaves like a regular CLI: usable by agents usable by humans usable from scripts / subprocess.run composable with grep, head, xargs, etc. Example: cli-use fs search_files --path /tmp --pattern "*.md" | head cli-use fs read_text_file --path /tmp/notes.md | grep TODO One thing I cared about a lot is agent ergonomics. Every add also emits a SKILL.md plus an AGENTS.md pointer, so agents working in a repo can discover and use the CLI automatically. A few details: pure Python stdlib zero runtime deps persistent aliases works with npm, pip, pipx, and local MCP servers built-in registry for common MCP servers I also benchmarked it against the real @modelcontextprotocol/server-filesystem server (14 tools). Depending on session size, token savings were around 60–80%, mostly by removing schema overhead and verbose request / response framing. It’s still alpha, but already usable. Repo: https://github.com/JustVugg/cli-use

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, 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
36%36% 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 · Strong signals: arr · Missing: mrr, revenue, profit
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.

Incorrect prediction on native model

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