home / mcp / wechat mcp server
WeChat-MCP: let Openclaw/Claude/ChatGPT and other AI assistants read and reply to WeChat for you
Configuration
View docs{
"mcpServers": {
"biboyqg-wechat-mcp": {
"command": "wechat-mcp",
"args": []
}
}
}You can automate WeChat on macOS using an MCP server that integrates with language models. It fetches messages, sends replies, posts Moments, and manages contacts through a structured MCP interface, enabling you to build conversational automations and workflows around WeChat chats.
You connect to the MCP server from your MCP client and issue high-level tool calls to interact with WeChat. Typical workflows include fetching recent messages from a chat, generating context-aware replies, sending those replies, and optionally publishing text-only Moments posts. You can also search chats, add contacts by WeChat ID, and run specialized sub-agents to assist with automation tasks. Start by running the server with a local stdio transport, then use your client to call the available tools to drive conversations and actions.
Prerequisites you need to have before installing:
Step 1: Install the MCP server package via Python’s package manager.
pip install wechat-mcp-serverStep 2: Start the MCP server using the stdio transport for local development.
wechat-mcp --transport stdioStep 3: If you prefer other transports, you can start the server with the HTTP or SSE transports by changing the transport flag.
wechat-mcp --transport streamable-http
```
```
wechat-mcp --transport sseGrant macOS Accessibility permissions so the MCP server can interact with WeChat and your terminal. Make sure WeChat is running before you start issuing commands.
Retrieve recent messages from a specified chat, enabling context for replies or analysis.
Send generated or scripted replies to a specific chat, automating conversation flow.
Add a new contact using a WeChat ID and send a friend request, enabling expanded communication.
Publish a text-only Moments post with an option to draft first without posting; supports text-only content.
Summarize chat history and extract key information to inform replies.
Auto-generate and send appropriate replies based on chat context.
Search chat history for specific content or keywords.
Monitor multiple chats and prioritize messages across conversations.
Analyze relationship dynamics and communication patterns within chats.