Redesign agent-facing CLIs and MCP servers by mining agent transcripts for workarounds
Treat the commands an agent runs immediately after calling your tool as bug reports and a product roadmap. Extract tool calls from your own agent transcripts, find repeated pipes (head, tail, grep, python, sort, wc) and repeated re-invocations with different arguments, then add selection flags, compact output modes, batch operations, loud failure summaries, and an agent mode that the skill file and help text teach. Validate by replaying recorded calls against old and new builds and measuring turns and correct answers, not just tokens.
online BOTH Product / E-commerce
From AI Engineer — Your Agent Hates Your CLI - Ayush Bhardwaj, Allos AI at 01:37
Problem: Agents work around friction silently instead of filing bug reports: they pipe away noise, reconstruct the same query by hand every time, and can even have a failed test masked as success because a pipe returns the last command's exit code or tail cuts the error itself. Tool authors never see this friction.
For: Developers building CLIs, MCP servers, and other tools that coding agents call — including their own products.
Products from this video
Examples
- chat-history (speaker's own CLI): 80% of session-open calls were piped into grep/head/tail/python; adding context-around-match, compact search, and multi-session inspect cut pipe usage from 82% to about a quarter
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