A Model Context Protocol (MCP) compliant server implementation for WeCom (WeChat Work) bot.
- Support for multiple message types:
- Text messages
- Markdown messages
- Image messages (base64)
- File messages
- @mention support (via user ID or phone number)
- Message history tracking
- Configurable logging system
- Full type annotations
- Pydantic-based data validation
- Python 3.10+
- WeCom Bot Webhook URL (obtained from WeCom group settings)
There are several ways to install WeCom Bot MCP Server:
npx -y @smithery/cli install wecom-bot-mcp-server --client claude- Install Cline Extension from VSCode marketplace
- Open Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
- Search for "Cline: Install Package"
- Type "wecom-bot-mcp-server" and press Enter
Add the server to your MCP client configuration file:
// For Claude Desktop on macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
// For Claude Desktop on Windows: %APPDATA%\Claude\claude_desktop_config.json
// For Windsurf: ~/.windsurf/config.json
// For Cline in VSCode: VSCode Settings > Cline > MCP Settings
{
"mcpServers": {
"wecom": {
"command": "uvx",
"args": [
"wecom-bot-mcp-server"
],
"env": {
"WECOM_WEBHOOK_URL": "your-webhook-url"
}
}
}
}# Windows PowerShell
$env:WECOM_WEBHOOK_URL = "your-webhook-url"
# Optional configurations
$env:MCP_LOG_LEVEL = "DEBUG" # Log levels: DEBUG, INFO, WARNING, ERROR, CRITICAL
$env:MCP_LOG_FILE = "path/to/custom/log/file.log" # Custom log file pathThe logging system uses platformdirs.user_log_dir() for cross-platform log file management:
- Windows:
C:\Users\<username>\AppData\Local\hal\wecom-bot-mcp-server\Logs - Linux:
~/.local/state/hal/wecom-bot-mcp-server/log - macOS:
~/Library/Logs/hal/wecom-bot-mcp-server
The log file is named mcp_wecom.log and is stored in the above directory.
You can customize the log level and file path using environment variables:
MCP_LOG_LEVEL: Set to DEBUG, INFO, WARNING, ERROR, or CRITICALMCP_LOG_FILE: Set to a custom log file path
Once configured, the MCP server runs automatically when your MCP client starts. You can interact with it through natural language in your AI assistant.
Scenario 1: Send weather information to WeCom
USER: "How's the weather in Shenzhen today? Send it to WeCom"
ASSISTANT: "I'll check Shenzhen's weather and send it to WeCom"
[The assistant will use the send_message tool to send the weather information]
Scenario 2: Send meeting reminder and @mention relevant people
USER: "Send a reminder for the 3 PM project review meeting, remind Zhang San and Li Si to attend"
ASSISTANT: "I'll send the meeting reminder"
[The assistant will use the send_message tool with mentioned_list parameter]
Scenario 3: Send a file
USER: "Send this weekly report to the WeCom group"
ASSISTANT: "I'll send the weekly report"
[The assistant will use the send_file tool]
Scenario 4: Send an image
USER: "Send this chart image to WeCom"
ASSISTANT: "I'll send the image"
[The assistant will use the send_image tool]
The server provides the following tools that your AI assistant can use:
-
send_message - Send text or markdown messages
- Parameters:
content,msg_type(text/markdown),mentioned_list,mentioned_mobile_list
- Parameters:
-
send_file - Send files to WeCom
- Parameters:
file_path
- Parameters:
-
send_image - Send images to WeCom
- Parameters:
image_path(local path or URL)
- Parameters:
If you want to use this package directly in your Python code (not as an MCP server):
from wecom_bot_mcp_server import send_message, send_wecom_file, send_wecom_image
# Send markdown message
await send_message(
content="**Hello World!**",
msg_type="markdown"
)
# Send text message and mention users
await send_message(
content="Hello @user1 @user2",
msg_type="text",
mentioned_list=["user1", "user2"]
)
# Send file
await send_wecom_file("/path/to/file.txt")
# Send image
await send_wecom_image("/path/to/image.png")- Clone the repository:
git clone https://github.com/loonghao/wecom-bot-mcp-server.git
cd wecom-bot-mcp-server- Create a virtual environment and install dependencies:
# Using uv (recommended)
pip install uv
uv venv
uv pip install -e ".[dev]"
# Or using traditional method
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -e ".[dev]"# Run all tests with coverage
uvx nox -s pytest
# Run import tests only
uvx nox -s test_imports
# Run specific test file
uvx nox -s pytest -- tests/test_message.py
# Run tests with verbose output
uvx nox -s pytest -- -v# Check code
uvx nox -s lint
# Automatically fix code style issues
uvx nox -s lint_fix# Build the package
uvx nox -s build
# Publish to PyPI (requires authentication)
uvx nox -s publishThe project uses GitHub Actions for CI/CD:
- MR Checks: Runs on all pull requests, tests on Ubuntu, Windows, and macOS with Python 3.10, 3.11, and 3.12
- Code Coverage: Uploads coverage reports to Codecov
- Import Tests: Ensures the package can be imported correctly after installation
All dependencies are automatically tested during CI to catch issues early.
wecom-bot-mcp-server/
├── src/
│ └── wecom_bot_mcp_server/
│ ├── __init__.py
│ ├── server.py
│ ├── message.py
│ ├── file.py
│ ├── image.py
│ ├── utils.py
│ └── errors.py
├── tests/
│ ├── test_server.py
│ ├── test_message.py
│ ├── test_file.py
│ └── test_image.py
├── docs/
├── pyproject.toml
├── noxfile.py
└── README.md
This project is licensed under the MIT License - see the LICENSE file for details.
- Author: longhao
- Email: hal.long@outlook.com

