MCPX – Turn any MCP server into a composable CLI for agents

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I built MCPX: https://github.com/lydakis/mcpx Core idea: MCPX turns MCP servers into Unix-composable commands for agent workflows. It is primarily for agents that are shell-first, and secondarily useful for humans running tools directly. For me, a practical use case is OpenClaw: OpenClaw can call `mcpx` like any normal CLI and use MCP servers immediately, without implementing custom MCP transport/auth plumbing in OpenClaw itself. This also fits well with Codex Apps mode, where connected apps can be exposed as MCP servers through the same command contract. Command contract: - mcpx - mcpx <server> - mcpx <server> <tool> Design choices: - schema-aware `--help` (inputs + declared outputs) - native flag surface from MCP `inputSchema` - pass-through tool output for normal shell composition (`jq`, `head`, pipes) - explicit exit mapping (`0/1/2/3`) - stdio + HTTP server support - optional caching - `mcpx add` for bootstrapping server config from install links/manifests/endpoints Examples: - mcpx github search-repositories --help - mcpx github search-repositories --query=mcp - echo '{"query":"mcp"}' | mcpx github search-repositories Install: - brew tap lydakis/mcpx && brew install --cask mcpx - npm i -g mcpx-go - pip install mcpx-go I would love feedback on command UX, schema/flag edge cases, and where this should stop vs expand.

Visit product View on Hacker NewsFeb 28, 2026

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

89%89% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · 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: ide, pipe, io · Missing: https docs, excited, just released

37%37% 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 · Missing: plus, platform, intuitive

32%32% 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

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

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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