WeCom Bot MCP Server

<div align="center"> <img src="wecom.png" alt="WeCom Bot Logo" width="200"/> </div>

A Model Context Protocol (MCP) compliant server implementation for WeCom (WeChat Work) bot.

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<a href="https://glama.ai/mcp/servers/amr2j23lbk"><img width="380" height="200" src="https://glama.ai/mcp/servers/amr2j23lbk/badge" alt="WeCom Bot Server MCP server" /></a>

Features

• 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

Requirements

• Python 3.10+
• WeCom Bot Webhook URL (obtained from WeCom group settings)

Installation

There are several ways to install WeCom Bot MCP Server:

1. Automated Installation (Recommended)

Using Smithery (For Claude Desktop):

npx -y @smithery/cli install wecom-bot-mcp-server --client claude

Using VSCode with Cline Extension:

1. Install Cline Extension from VSCode marketplace
2. Open Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
3. Search for "Cline: Install Package"
4. Type "wecom-bot-mcp-server" and press Enter

2. Manual Installation

Install from PyPI:

pip install wecom-bot-mcp-server

Configure MCP manually:

Create or update your MCP configuration file:

// For Windsurf: ~/.windsurf/config.json
{
  "mcpServers": {
    "wecom": {
      "command": "uvx",
      "args": [
        "wecom-bot-mcp-server"
      ],
      "env": {
        "WECOM_WEBHOOK_URL": "your-webhook-url"
      }
    }
  }
}

Configuration

Setting Environment Variables

# 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 path

Log Management

The logging system uses platformdirs.user_log_dir() for cross-platform log file management:

• Windows: C:\Users\<username>\AppData\Local\hal\wecom-bot-mcp-server
• Linux: ~/.local/share/hal/wecom-bot-mcp-server
• macOS: ~/Library/Application Support/hal/wecom-bot-mcp-server

The log file is named mcp_wecom.log and is stored in the above directory.

Usage

Starting the Server

wecom-bot-mcp-server

Usage Examples (With MCP)

# 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"

await mcp.send_message(
    content="Shenzhen Weather:\n- Temperature: 25°C\n- Weather: Sunny\n- Air Quality: Good",
    msg_type="markdown"
)

# 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"

await mcp.send_message(
    content="## Project Review Meeting Reminder\n\nTime: Today 3:00 PM\nLocation: Meeting Room A\n\nPlease be on time!",
    msg_type="markdown",
    mentioned_list=["zhangsan", "lisi"]
)

# Scenario 3: Send a file
USER: "Send this weekly report to the WeCom group"
ASSISTANT: "I'll send the weekly report"

await mcp.send_message(
    content=Path("weekly_report.docx"),
    msg_type="file"
)

Direct API Usage

Send Messages

from wecom_bot_mcp_server import mcp

# Send markdown message
await mcp.send_message(
    content="**Hello World!**", 
    msg_type="markdown"
)

# Send text message and mention users
await mcp.send_message(
    content="Hello @user1 @user2",
    msg_type="text",
    mentioned_list=["user1", "user2"]
)

Send Files

from wecom_bot_mcp_server import send_wecom_file

# Send file
await send_wecom_file("/path/to/file.txt")

Send Images

from wecom_bot_mcp_server import send_wecom_image

# Send local image
await send_wecom_image("/path/to/image.png")

# Send URL image
await send_wecom_image("https://example.com/image.png")

Development

Setup Development Environment

1. Clone the repository:

git clone https://github.com/loonghao/wecom-bot-mcp-server.git
cd wecom-bot-mcp-server

2. 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]"

Testing

# Using uv (recommended)
uvx nox -s pytest

# Or using traditional method
nox -s pytest

Code Style

# Check code
uvx nox -s lint

# Automatically fix code style issues
uvx nox -s lint_fix

Building and Publishing

# Build the package
uv build

# Build and publish to PyPI
uv build && twine upload dist/*

Project Structure

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

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

• Author: longhao
• Email: hal.long@outlook.com