Klavis AI 开源MCP集成平台深度测评

# 1. 安装Docker Desktop for Windows
# 下载地址：https://www.docker.com/products/docker-desktop/

# 2. 克隆仓库
git clone https://github.com/klavis-ai/klavis.git
cd klavis

# 3. 启动MCP服务器（以报告生成为例）
cd mcp_servers/report_generation
docker-compose up --build

# 4. 验证服务器运行
# 服务器应运行在 http://localhost:8000

# 1. 安装Docker Desktop for Mac
# 下载地址：https://www.docker.com/products/docker-desktop/

# 2. 克隆仓库并部署
git clone https://github.com/klavis-ai/klavis.git
cd klavis

# 3. 使用Docker Compose启动服务
docker-compose -f docker-compose.mac.yml up --build

# 1. 安装Homebrew（如未安装）
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# 2. 安装Python和Node.js
brew install python nodejs

# 3. 创建Python虚拟环境
python -m venv klavis-env
source klavis-env/bin/activate

# 4. 安装Klavis AI
pip install klavis-ai

# 1. 安装依赖
sudo apt update
sudo apt install -y docker.io docker-compose nodejs npm python3 python3-pip

# 2. 启动Docker服务
sudo systemctl start docker
sudo systemctl enable docker

# 3. 克隆并部署Klavis AI
git clone https://github.com/klavis-ai/klavis.git
cd klavis

# 4. 部署所有服务
sudo docker-compose up -d

# 1. 安装依赖
sudo yum install -y yum-utils device-mapper-persistent-data lvm2
sudo yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo
sudo yum install -y docker-ce nodejs npm

# 2. 启动Docker
sudo systemctl start docker
sudo systemctl enable docker

# 3. 安装Docker Compose
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose

# Windows/macOS：确保Docker Desktop正在运行
# Linux：重启Docker服务
sudo systemctl restart docker

# 修改docker-compose.yml中的端口映射
ports:
  - "新端口:容器端口"

# 将用户添加到docker组
sudo usermod -aG docker $USER
# 重新登录生效

# 进入Discord机器人目录
cd mcp_clients/discord_bot

# 配置环境变量
echo "DISCORD_TOKEN=你的机器人Token" > .env
echo "OPENAI_API_KEY=你的OpenAI API密钥" >> .env

# 启动服务
docker-compose up --build

# 进入Slack机器人目录
cd mcp_clients/slack_bot

# 配置环境变量
echo "SLACK_BOT_TOKEN=xoxb-你的机器人Token" > .env
echo "SLACK_SIGNING_SECRET=你的签名密钥" >> .env

# 启动服务
docker-compose up --build

# 启动Web客户端
cd web_client

# 安装依赖
npm install

# 配置环境变量
cp .env.example .env
# 编辑.env文件，添加必要的API密钥和配置

# 启动开发服务器
npm run dev

# 构建生产版本
npm run build

# mcp_servers/report_generation/app.py
import os
import asyncio
from typing import List, Optional
import pandas as pd
import matplotlib.pyplot as plt
from mcp import MCPServer, Tool, mcp_tool
import mysql.connector
from datetime import datetime, timedelta
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.application import MIMEApplication

class ReportGenerationServer(MCPServer):
    def __init__(self):
        super().__init__("report-generation")
        self.register_tool(self.generate_sales_report)
        self.register_tool(self.send_slack_report)
        self.register_tool(self.export_to_pdf)

    @mcp_tool
    async def generate_sales_report(
        self, 
        start_date: str,
        end_date: str,
        report_type: str = "daily"
    ) -> str:
        """生成销售报告工具
        
        Args:
            start_date: 开始日期 (YYYY-MM-DD)
            end_date: 结束日期 (YYYY-MM-DD)
            report_type: 报告类型 (daily, weekly, monthly)
        """
        try:
            # 连接数据库
            db_connection = mysql.connector.connect(
                host=os.getenv('DB_HOST', 'localhost'),
                user=os.getenv('DB_USER', 'root'),
                password=os.getenv('DB_PASSWORD', ''),
                database=os.getenv('DB_NAME', 'sales')
            )
            
            # 查询销售数据
            query = """
                SELECT 
                    DATE(created_at) as date,
                    COUNT(*) as order_count,
                    SUM(amount) as total_sales,
                    AVG(amount) as average_order_value
                FROM orders 
                WHERE created_at BETWEEN %s AND %s
                GROUP BY DATE(created_at)
                ORDER BY date
            """
            
            df = pd.read_sql(query, db_connection, params=(start_date, end_date))
            
            # 生成报告摘要
            report = f"""
# 销售报告 ({start_date} 至 {end_date})

## 概览
- 总订单数: {df['order_count'].sum():,}
- 总销售额: ${df['total_sales'].sum():,.2f}
- 平均订单价值: ${df['average_order_value'].mean():.2f}

## 每日表现
{df.to_string(index=False)}

## 关键指标
- 最佳销售日: {df.loc[df['total_sales'].idxmax(), 'date']} (${df['total_sales'].max():,.2f})
- 平均每日订单: {df['order_count'].mean():.1f}
            """
            
            # 生成图表
            plt.figure(figsize=(10, 6))
            plt.plot(pd.to_datetime(df['date']), df['total_sales'])
            plt.title(f'Sales Trend ({start_date} to {end_date})')
            plt.xlabel('Date')
            plt.ylabel('Sales ($)')
            plt.xticks(rotation=45)
            plt.tight_layout()
            plt.savefig('/tmp/sales_trend.png')
            
            db_connection.close()
            return report
            
        except Exception as e:
            return f"生成报告时出错: {str(e)}"

    @mcp_tool
    async def send_slack_report(
        self,
        report_content: str,
        channel: str = "#reports"
    ) -> str:
        """通过Slack发送报告工具
        
        Args:
            report_content: 报告内容
            channel: Slack频道
        """
        try:
            from slack_sdk import WebClient
            from slack_sdk.errors import SlackApiError
            
            slack_token = os.getenv('SLACK_BOT_TOKEN')
            client = WebClient(token=slack_token)
            
            # 发送消息
            response = client.chat_postMessage(
                channel=channel,
                text="每日销售报告",
                blocks=[
                    {
                        "type": "header",
                        "text": {
                            "type": "plain_text",
                            "text": "📊 每日销售报告"
                        }
                    },
                    {
                        "type": "section",
                        "text": {
                            "type": "mrkdwn",
                            "text": report_content[:3000]  # Slack消息长度限制
                        }
                    },
                    {
                        "type": "image",
                        "title": {
                            "type": "plain_text",
                            "text": "销售趋势图"
                        },
                        "block_id": "image4",
                        "image_url": "http://your-server/sales_trend.png",  # 需要实际部署图片服务
                        "alt_text": "销售趋势图"
                    }
                ]
            )
            
            return f"报告已成功发送到 {channel}"
            
        except Exception as e:
            return f"发送Slack消息时出错: {str(e)}"

    @mcp_tool
    async def export_to_pdf(
        self,
        report_content: str,
        output_path: str = "/tmp/report.pdf"
    ) -> str:
        """将报告导出为PDF工具
        
        Args:
            report_content: 报告内容
            output_path: 输出PDF路径
        """
        try:
            from weasyprint import HTML
            import tempfile
            
            # 创建临时HTML文件
            with tempfile.NamedTemporaryFile(mode='w', suffix='.html', delete=False) as f:
                html_content = f"""
                <!DOCTYPE html>
                <html>
                <head>
                    <meta charset="utf-8">
                    <style>
                        body {{ font-family: Arial, sans-serif; margin: 40px; }}
                        h1 {{ color: #333; border-bottom: 2px solid #333; }}
                        h2 {{ color: #666; }}
                        table {{ border-collapse: collapse; width: 100%; }}
                        th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
                        th {{ background-color: #f2f2f2; }}
                    </style>
                </head>
                <body>
                    {report_content.replace('\n', '<br>')}
                </body>
                </html>
                """
                f.write(html_content)
                temp_html_path = f.name
            
            # 转换为PDF
            HTML(temp_html_path).write_pdf(output_path)
            
            # 清理临时文件
            os.unlink(temp_html_path)
            
            return f"PDF报告已保存到: {output_path}"
            
        except Exception as e:
            return f"导出PDF时出错: {str(e)}"

async def main():
    server = ReportGenerationServer()
    await server.run()

if __name__ == "__main__":
    asyncio.run(main())

# docker-compose.yml
version: '3.8'

services:
  report-generation:
    build: .
    ports:
      - "8001:8000"
    environment:
      - DB_HOST=localhost
      - DB_USER=root
      - DB_PASSWORD=your_password
      - DB_NAME=sales
      - SLACK_BOT_TOKEN=xoxb-your-token
    volumes:
      - /tmp:/tmp
    restart: unless-stopped

  discord-bot:
    build: ./mcp_clients/discord_bot
    environment:
      - DISCORD_TOKEN=your-discord-token
      - MCP_SERVER_URL=http://report-generation:8000
    depends_on:
      - report-generation
    restart: unless-stopped

# .env 文件
# 数据库配置
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=your_password
DB_NAME=sales

# Slack配置
SLACK_BOT_TOKEN=xoxb-your-slack-token

# Discord配置
DISCORD_TOKEN=your-discord-token

# API配置
OPENAI_API_KEY=your-openai-api-key

# 在Discord或Slack中使用命令
/report generate start_date=2024-01-01 end_date=2024-01-31

# 或者通过HTTP API调用
curl -X POST "http://localhost:8001/tools/generate_sales_report" \
  -H "Content-Type: application/json" \
  -d '{
    "start_date": "2024-01-01",
    "end_date": "2024-01-31"
  }'