Open Wot 1.0.5 Replaces MCP Config Guessing with Two Commands
AI coding tools are only as good as the component knowledge they can reach, and the friction of wiring up MCP servers across different editors has become the new bottleneck. Open Wot 1.0.5 treats integration as a diagnosable, reversible operation rather than a one-shot config dump, which matters for any team that wants AI-generated UI code to use real, version-aligned component APIs instead of hallucinated props.
The release adds `wot agent init` and `wot agent doctor` commands that write MCP server configs, install a component-selection Skill, and inject project-level instructions so AI coding agents query real component APIs before generating code. It supports Cursor, Claude Code, VS Code, and Codex out of the box, with a unified `--client` flag replacing four different config formats.
A `doctor` command goes beyond checking that files were written: it performs a live MCP handshake, confirms the eight wot-ui tools are present, and reports client-side registration status where possible. The CLI defaults to `npx`-based commands to avoid PATH mismatches between terminals and desktop apps, and all file modifications run through a ChangePlan with dry-run support, idempotent writes, and atomic rollbacks.
MCP tool responses are now summary-first, returning only names and descriptions until an agent explicitly requests full props or demo source code. A separate `starter-cleaner` Skill strips wot-starter v2 templates down to a minimal project while keeping the UI and charting dependencies intact.
Manual MCP configuration has quietly become the new "works on my machine" problem for AI coding — the toolchain is ready, but wiring it into each editor's idiosyncratic config format and scope rules is where developers burn time.
Defaulting to `npx` for MCP server commands is a pragmatic trade-off: it adds a cold-start delay but eliminates an entire class of PATH-related support issues that desktop apps inherit from their launch environment.
The combination of MCP (data), Skill (query strategy), and Instructions (behavioral habit) acknowledges that feeding AI accurate component knowledge requires solving three distinct problems, not just dumping a JSON schema into a config file.
Treating configuration as a lifecycle with init, status, doctor, and remove commands — plus atomic writes and rollbacks — raises the bar for CLI tools that modify project files, especially when those files are also hand-edited by developers.
Great UI library. It would be even better if it weren't limited to running on uni-app.