by BiboyQG · MCP Server · ★ 262
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install wechat-mcp-server
claude mcp add --transport stdio wechat-mcp -- wechat-mcpTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 262 |
| Forks | 80 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 76/100 |
| Open Issues | 10 |
| Last Updated | 2026-03-09 |
| Created | 2025-12-04 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~34k |
WeChat MCP Server 中文 | English An MCP server that automates WeChat on macOS using the Accessibility API and screen capture. It enables LLMs to interact with WeChat chats programmatically. Features 📨 Fetch recent messages from any chat (contact or group) ✍️ Send automated replies based on chat history 📷 Publish text-only Moments posts, with optional draft-only mode 👥 Add contacts using WeChat...
Looking for a WeChat-MCP alternative? If you're comparing WeChat-MCP with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
🔍微信聊天数据分析的本地化AI-agent(Docker/Windows/MacOS) · AI分身 / 大模型分析 / 好友排行 / 词云 / 情感趋势 / 群聊画像
a simple openclaw desktop u
🐾 ClawPanel — OpenClaw AI 助手可视化管理面板。Go 单二进制部署，支持 20+ 通道统一管理，跨平台，实时日志监控。
Reverse-engineered TypeScript client for QClaw's WeChat Access API.
企业微信 AI 机器人插件 - Enterprise WeChat (WeCom) AI Bot plugin for OpenClaw. 支持流式输出、动态 Agent 管理、群聊集成、指令白名单等功能。
现已同时支持QClaw和WorkBuddy方式登录！微信包装了开源项目OpenClaw，所以我们把他的Channel抽出来给OpenClaw用了😄
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: