TRAECNclaw MCP
Control TraeCN desktop automation through explicit, profile-scoped MCP tools. Supports task delegation, model control, dialog handling, code review, and unattended workflows.
README
TRAECNclaw MCP Skill
Status: Thin Public Distribution Repository
This repository is a generated public distribution mirror.
- Canonical source: Luckycat133/TRAECNclaw
- Canonical path:
skills/traecnclaw-mcp - Mirrored path:
.codex/skills/traecnclaw-mcp - Exact source revision:
SOURCE_REVISION
Do not edit the mirrored Skill by hand. Behavior, version, tests, MCP code, npm packages, and release artifacts are produced from TRAECNclaw.
What stays in Git
- the public Agent Skill source
- installation documentation
- source-revision metadata
- marketplace metadata and release notes
- automation that verifies the mirror
Generated .tgz, .zip, npm-ready directories, and checksum manifests belong
in GitHub Releases,
not in the default branch.
Installation
TRAECNclaw MCP is published across several channels. Pick the one that fits
your client — they all serve the same traecnclaw-mcp stdio server.
Option 1 — Glama (recommended, one-click)
Open the Glama server page and click Install; Glama reads glama.json
from this repo and emits a paste-ready MCP config for Claude Desktop, Cursor,
and more.
- https://glama.ai/mcp/servers/@Luckycat133/traecnclaw-mcp-skill
Option 2 — npm (global install)
npm install -g traecnclaw@0.3.1
This puts the traecnclaw-mcp stdio server on your PATH. Then register it
with your client using the config below.
Option 3 — npx (no install)
For clients that can launch via npx:
{
"mcpServers": {
"traecn": {
"command": "npx",
"args": ["-y", "traecnclaw@0.3.1", "traecnclaw-mcp"],
"env": { "TRAECN_MCP_TOOL_PROFILE": "public" }
}
}
}
Option 4 — MCP Registry
The server is indexed in the official Model Context Protocol Registry as
io.github.Luckycat133/traecnclaw (v0.3.1). Registry-aware clients can
discover and install it automatically.
Option 5 — Smithery (Codex / Cursor / OpenClaw Skill)
Install the packaged Skill for AI coding clients; the page offers one-click install for Codex, OpenClaw, Cursor, and GitHub Copilot.
- https://smithery.ai/skills/xingmiao201081/traecnclaw-mcp
MCP client configuration
Once the server binary is available (any of Options 2–3), register it with
your client. Example mcp.json / claude_desktop_config.json:
{
"mcpServers": {
"traecn": {
"command": "traecnclaw-mcp",
"env": {
"TRAECN_HOST": "127.0.0.1",
"TRAECN_PORT": "8788",
"TRAECN_GATEWAY_TOKEN": "",
"TRAECN_MCP_TOOL_PROFILE": "public"
}
}
}
}
Keep the gateway on 127.0.0.1 unless remote access is intentional. Use a
token for any shared or non-local environment.
Install the Skill (from a cloned checkout)
mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
cp -R .codex/skills/traecnclaw-mcp "${CODEX_HOME:-$HOME/.codex}/skills/"
For release archives, download the matching asset from the Releases page and verify it against the release checksum manifest before extracting it.
Release provenance
Every public release must identify the canonical TRAECNclaw commit that produced it. A mirror update is valid only when the mirrored Skill matches that canonical path byte-for-byte.
Glama, Smithery, and the MCP Registry metadata are retained and already
deployed; this mirror stays in sync via the Sync canonical Skill workflow.
License
MIT
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