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A Python server implementation for WeCom (WeChat Work) bot that follows the Model Context Protocol (MCP). This server provides a standardized interface for handling automated messaging and context-aware interactions within enterprise WeChat environments.

Publisherloonghao
Repositorywecom-bot-mcp-server
LanguagePython
Forks
17
Stars
86
Available tools
0
Transport typestdio
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LicenseMIT
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  • Connect tools to AI workflows

    WeCom exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    86 stars and 17 forks from the linked repository.

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WeCom Bot MCP Server

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

PyPI version Python Version codecov Code style: ruff smithery badge

English | 中文

Features

  • Support for multiple message types:
    • Markdown messages (with @mentions and font colors)
    • Markdown V2 messages (with tables, lists, embedded images)
    • Image messages (base64/local file/URL)
    • File messages
    • Template card messages (text_notice and news_notice)
  • Multi-bot support: Configure and use multiple WeCom bots
  • @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):

bash
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 Configuration

Add the server to your MCP client configuration file:

json
// 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"
      }
    }
  }
}

Configuration

Setting Environment Variables

Single Bot (Default)

bash
# 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

Multiple Bots Configuration

You can configure multiple bots using any of these methods:

Method 1: JSON Configuration (Recommended)

bash
# Windows PowerShell
$env:WECOM_BOTS = '{"alert": {"name": "Alert Bot", "webhook_url": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=xxx", "description": "For alerts"}, "ci": {"name": "CI Bot", "webhook_url": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=yyy", "description": "For CI/CD"}}'

# Linux/macOS
export WECOM_BOTS='{"alert": {"name": "Alert Bot", "webhook_url": "https://...", "description": "For alerts"}, "ci": {"name": "CI Bot", "webhook_url": "https://...", "description": "For CI/CD"}}'

Method 2: Individual Environment Variables

bash
# Windows PowerShell
$env:WECOM_BOT_ALERT_URL = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=xxx"
$env:WECOM_BOT_CI_URL = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=yyy"
$env:WECOM_BOT_NOTIFY_URL = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=zzz"

Method 3: Combined Mode

bash
# WECOM_WEBHOOK_URL becomes the "default" bot
$env:WECOM_WEBHOOK_URL = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=default"
# Additional bots
$env:WECOM_BOT_ALERT_URL = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=alert"

MCP Client Configuration with Multiple Bots

json
{
  "mcpServers": {
    "wecom": {
      "command": "uvx",
      "args": ["wecom-bot-mcp-server"],
      "env": {
        "WECOM_WEBHOOK_URL": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=default",
        "WECOM_BOTS": "{\"alert\": {\"name\": \"Alert Bot\", \"webhook_url\": \"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=alert\"}, \"ci\": {\"name\": \"CI Bot\", \"webhook_url\": \"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=ci\"}}"
      }
    }
  }
}

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\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 CRITICAL
  • MCP_LOG_FILE: Set to a custom log file path

Usage

Once configured, the MCP server runs automatically when your MCP client starts. You can interact with it through natural language in your AI assistant.

Usage Examples

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]

Available MCP Tools

The server provides the following tools that your AI assistant can use:

  1. send_message - Send text or markdown messages

    • Parameters: content, msg_type (markdown/markdown_v2), mentioned_list, mentioned_mobile_list, bot_id
    • markdown: Use when content contains <@userid> mentions or font colors. The <@userid> syntax is WeCom's official mention format, which avoids conflicts with email addresses like @user@email.com
    • markdown_v2: Use for tables, lists, embedded images, or general content (default)
  2. send_wecom_file - Send files to WeCom

    • Parameters: file_path, bot_id
  3. send_wecom_image - Send images to WeCom

    • Parameters: image_path (local path or URL), bot_id
  4. send_wecom_template_card_text_notice - Send text notice template card

    • Parameters: template_card_source, template_card_main_title, template_card_card_action, bot_id, and optional fields
    • Use for notifications with emphasis content, quotes, and action buttons
  5. send_wecom_template_card_news_notice - Send news notice template card

    • Parameters: template_card_source, template_card_main_title, template_card_card_action, template_card_image, bot_id, and optional fields
    • Use for news-style notifications with images and rich content
  6. list_wecom_bots - List all configured bots

    • Returns: List of available bots with their IDs, names, and descriptions

Multi-Bot Usage Examples

Scenario 5: Send alert to specific bot

USER: "Send a critical alert to the alert bot: Server CPU usage is above 90%"
ASSISTANT: "I'll send the alert to the alert bot"
[The assistant will use send_message with bot_id="alert"]

Scenario 6: List available bots

USER: "What WeCom bots are available?"
ASSISTANT: "Let me check the available bots"
[The assistant will use list_wecom_bots tool]

Scenario 7: Send CI notification

USER: "Send build success notification to the CI bot"
ASSISTANT: "I'll send the notification to the CI bot"
[The assistant will use send_message with bot_id="ci"]

Scenario 8: Send template card notification

USER: "Send a deployment success notification card with a link to the dashboard"
ASSISTANT: "I'll send a template card notification"
[The assistant will use send_wecom_template_card_text_notice tool]

Scenario 9: Send news-style notification

USER: "Send a news card about the new feature release with an image"
ASSISTANT: "I'll send a news notice card"
[The assistant will use send_wecom_template_card_news_notice tool]

For Developers: Direct API Usage

If you want to use this package directly in your Python code (not as an MCP server):

python
from wecom_bot_mcp_server import send_message, send_wecom_file, send_wecom_image, send_wecom_template_card

# Send markdown message (uses default bot)
await send_message(
    content="**Hello World!**",
    msg_type="markdown"
)

# Send markdown_v2 message with tables and lists (default)
await send_message(
    content="| Column1 | Column2 |\n|---------|---------|\\n| Value1  | Value2  |",
    msg_type="markdown_v2"
)

# Send text message and mention users (use markdown for @mentions)
await send_message(
    content="Hello <@user1> <@user2>",
    msg_type="markdown",
    mentioned_list=["user1", "user2"]
)

# Send message to a specific bot
await send_message(
    content="Build completed successfully!",
    msg_type="markdown_v2",
    bot_id="ci"  # Send to CI bot
)

# Send alert to alert bot
await send_message(
    content="⚠️ High CPU usage detected!",
    msg_type="markdown_v2",
    bot_id="alert"
)

# Send file to specific bot
await send_wecom_file("/path/to/file.txt", bot_id="ci")

# Send image to specific bot
await send_wecom_image("/path/to/image.png", bot_id="alert")

# Send template card (text_notice)
await send_wecom_template_card(
    template_card_type="text_notice",
    template_card_source={"icon_url": "https://example.com/icon.png", "desc": "System"},
    template_card_main_title={"title": "Deployment Success", "desc": "Production environment"},
    template_card_card_action={"type": 1, "url": "https://example.com/dashboard"},
    template_card_emphasis_content={"title": "100%", "desc": "Success Rate"},
    bot_id="ci"
)

Multi-Bot Configuration in Code

python
from wecom_bot_mcp_server.bot_config import get_bot_registry, list_available_bots

# List all available bots
bots = list_available_bots()
for bot in bots:
    print(f"Bot: {bot['id']} - {bot['name']}")

# Check if a specific bot exists
registry = get_bot_registry()
if registry.has_bot("alert"):
    print("Alert bot is configured")

# Get webhook URL for a specific bot
url = registry.get_webhook_url("ci")

Development

Setup Development Environment

  1. Clone the repository:
bash
git clone https://github.com/loonghao/wecom-bot-mcp-server.git
cd wecom-bot-mcp-server
  1. Create a virtual environment and install dependencies:
bash
# 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

bash
# 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

Code Style

bash
# Check code
uvx nox -s lint

# Automatically fix code style issues
uvx nox -s lint_fix

Building and Publishing

bash
# Build the package
uvx nox -s build

# Publish to PyPI (requires authentication)
uvx nox -s publish

Continuous Integration

The 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.

Project Structure

wecom-bot-mcp-server/
├── src/
│   └── wecom_bot_mcp_server/
│       ├── __init__.py
│       ├── __main__.py
│       ├── __version__.py
│       ├── app.py           # FastMCP application setup
│       ├── server.py        # Server entry point
│       ├── message.py       # Message and template card handling
│       ├── file.py          # File upload handling
│       ├── image.py         # Image upload handling
│       ├── bot_config.py    # Multi-bot configuration
│       ├── utils.py         # Utility functions
│       ├── log_config.py    # Logging configuration
│       └── errors.py        # Error definitions
├── tests/
│   ├── test_server.py
│   ├── test_message.py
│   ├── test_file.py
│   ├── test_image.py
│   └── test_bot_config.py
├── docs/
├── pyproject.toml
├── noxfile.py
└── README.md

License

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

Contact

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "wecom": {
      "command": "uvx",
      "args": [
        "wecom-bot-mcp-server"
      ],
      "env": {
        "WECOM_WEBHOOK_URL": "your-webhook-url"
      }
    }
  }
}

Use WeCom MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once WeCom is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add WeCom as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the WeCom MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "wecom": {
      "command": "npx",
      "args": [
        "-y",
        "wecom-bot-mcp-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the WeCom MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the WeCom MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use WeCom to help me with this task?
WeCom
Sure. I read it.
Here is what I found using WeCom.

Frequently asked questions

What is the WeCom MCP server used for?

WeCom is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use WeCom MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use WeCom with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use WeCom MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With WeCom connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect WeCom MCP to TypingMind?

WeCom runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does WeCom MCP provide in TypingMind?

WeCom exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use WeCom MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If WeCom requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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