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.

Related MCP server: heddle

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