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MCP Server · 办公协作

Gmail MCP Agent

一个企业级的潜在客户培育系统,通过Gmail自动化外联活动,包括智能跟进、响应跟踪和24/7运行。

stdio官方级别未说明来源级核验工具 0
PythonGmail集成团队协作

快速开始

作者 / 组织
brandononchain
提供方
brandononchain

工具数量

0

来源目录标注

资源数量

0

来源目录标注

提示词数量

0

来源目录标注

GitHub Star

0

仓库热度

收录来源

2 个

MCP World

选型结论

Gmail MCP Agent 属于办公协作类 MCP Server,公开资料标注 0 个工具,通过stdio 本地进程接入,客户端适配情况以官方文档为准,有可以直接复制的安装命令;接入前要先准备 访问 Token。

分类

办公协作

传输方式

stdio

认证方式

访问 Token

部署方式

公开资料未说明

安装与配置

安装命令

pip install -r requirements.txt

公开命令不是通用的启动方式,拼不出可靠配置示例;请在客户端 MCP 配置里按官方文档填写 command 和 args。

上手步骤

  1. 1

    准备运行环境

    stdio 服务在本地起进程,需要先装好 对应的运行时,再按安装命令拉取依赖。

  2. 2

    安装服务

    复制下面的安装命令执行;命令里带占位符的位置换成自己的密钥或路径。

  3. 3

    写入客户端配置

    在客户端的 MCP 配置里填启动命令和参数,保存后完全退出客户端再打开,让新配置生效。

  4. 4

    验证是否可用

    连接成功后客户端会列出 0 个工具,随便调一个只读工具确认能返回结果。

接入前确认

不要直接授予不必要的文件、网络或账号权限;先核对安装命令和配置内容。

详细介绍

🤖 Gmail MCP Agent - 24/7 Lead Nurturing System

A comprehensive, enterprise-grade lead nurturing system that automates Gmail outreach campaigns with intelligent follow-ups, response tracking, and 24/7 operation via MCP (Model Context Protocol) server.

🚀 Features

✅ Automated Lead Nurturing

  • 24/7 Operation - Runs continuously with Docker containerization
  • Intelligent Follow-ups - Automatic sequences at 3 days and 7 days
  • Response Tracking - Monitors Gmail for replies and categorizes them
  • Lead Scoring - Tracks engagement and interest levels
  • Smart Responses - Automatically responds to interested leads

📊 MCP Server Architecture

  • Remote Control - Control system via MCP protocol
  • Real-time Monitoring - Live status and performance tracking
  • Docker Deployment - Production-ready containerization
  • Health Checks - Automatic recovery and error handling
  • Scalable Design - Ready for enterprise use

🎯 Email Campaign Management

  • CSV-based Lead Lists - Easy contact management
  • Template System - Jinja2-powered email personalization
  • Rate Limiting - Respects Gmail API quotas
  • Resume Capability - Continue from where you left off
  • Comprehensive Logging - Complete audit trail

📁 Project Structure

├── send_from_csv.py          # Main Gmail sender script
├── lead_nurturer.py          # Automated nurturing system
├── mcp_server.py             # 24/7 MCP server
├── mcp_client.py             # Control interface
├── lead_dashboard.py         # Monitoring dashboard
├── run_nurturing.py          # Automation runner
├── contacts.csv              # Lead database (96 dental practices)
├── body.txt                  # Email template
├── credentials.json          # Gmail API credentials
├── nurturing_config.json     # System configuration
├── gmail_sync_state.json     # Gmail incremental sync state (auto-created)
├── requirements.txt          # Python dependencies
├── Dockerfile               # Container configuration
├── docker-compose.yml       # Deployment setup
├── deploy.sh                # One-click deployment
└── DEPLOYMENT_GUIDE.md      # Complete setup guide

🛠️ Quick Start

1. Clone and Setup

git clone https://github.com/brandononchain/GMAIL-MCP-Agent.git
cd GMAIL-MCP-Agent
pip install -r requirements.txt

2. Configure Gmail API

  • Get OAuth2 credentials from Google Cloud Console
  • Save as credentials.json
  • Update sender email in nurturing_config.json

3. Deploy 24/7 System

# Docker deployment (recommended)
./deploy.sh

# Or manual deployment
docker-compose up -d

4. Start Nurturing

# Using MCP client
python mcp_client.py start 4

# Or direct execution
python run_nurturing.py

🎮 Control Commands

MCP Client Interface

# Start nurturing system (every 4 hours)
python mcp_client.py start 4

# Check system status
python mcp_client.py status

# Get lead report
python mcp_client.py report

# Send test email
python mcp_client.py test your-email@example.com

# View recent logs
python mcp_client.py logs 100

# Stop the system
python mcp_client.py stop

Direct Scripts

# Run single nurturing cycle
python lead_nurturer.py

# View lead dashboard
python lead_dashboard.py

# Send emails from CSV
python send_from_csv.py contacts.csv --body_file body.txt

📊 Current Campaign

Dental Practice Outreach

  • Target: 96 dental practices in Chicago
  • Message: AI lead follow-up system for dental practices
  • Follow-up Schedule: 3 days and 7 days after initial contact
  • Expected Results: 20-30% response rate, 10-15% conversion

Email Template

Hi {{first_name}},

Did you know many dental practices lose 20–30% of new patient inquiries because follow-ups slip through the cracks?

We've built an AI agent that automatically follows up with every lead via SMS/email and books them straight into your calendar.

Clients typically see 5–9 extra appointments in the first 30 days.

Have time for 10-min demo call this week?

Thank you,
Brandon
Quantra Labs

🔧 Configuration

Environment Variables

# Gmail API Configuration
CREDENTIALS_FILE=credentials.json
TOKEN_FILE=token.json

# Nurturing Settings
PER_MINUTE=12
RESUME=false
LOG_FILE=send_log.csv

# MCP Server Settings
MCP_SERVER_PORT=8000
LOG_LEVEL=INFO

Nurturing Configuration

{
  "sender_email": "your-email@domain.com",
  "follow_up_schedule": {
    "followup_1_days": 3,
    "followup_2_days": 7
  },
  "automation": {
    "check_responses_interval_hours": 4,
    "auto_respond_to_interest": true
  }
}

📈 Performance Metrics

Expected Results

  • Response Rate: 20-30% from initial outreach
  • Follow-up Response: 40-60% from follow-ups
  • Conversion Rate: 10-15% to interested leads
  • Automation Coverage: 80% of responses handled automatically
  • Uptime: 99.9% with Docker restart policies

Monitoring

  • Real-time lead scoring and status tracking
  • Response rate analytics and conversion metrics
  • System health monitoring and error reporting
  • Complete audit trail of all interactions

🚀 Deployment Options

Docker (Recommended)

# One-click deployment
./deploy.sh

# Manual deployment
docker-compose up -d

Local Development

# Install dependencies
pip install -r requirements.txt

# Run nurturing system
python run_nurturing.py

Production Server

# Systemd service
sudo cp lead-nurturing.service /etc/systemd/system/
sudo systemctl enable lead-nurturing
sudo systemctl start lead-nurturing

🔒 Security & Privacy

  • Local Data Storage - All data remains on your server
  • OAuth2 Authentication - Secure Gmail API access
  • No External Services - No data sent to third parties
  • Encrypted Credentials - Secure credential management
  • Audit Logging - Complete activity tracking

📞 Support & Documentation

  • Deployment Guide: DEPLOYMENT_GUIDE.md
  • Nurturing Guide: NURTURING_README.md
  • Debug Report: DEBUG_REPORT.md
  • Docker Setup: docker-compose.yml

🎯 Use Cases

Sales Outreach

  • B2B lead generation and nurturing
  • Automated follow-up sequences
  • Response tracking and lead scoring

Marketing Campaigns

  • Email marketing automation
  • A/B testing and optimization
  • Performance analytics

Customer Success

  • Onboarding email sequences
  • Renewal and upsell campaigns
  • Customer feedback collection

📊 System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │◄──►│   MCP Server     │◄──►│  Lead Nurturer  │
│  (Control)      │    │  (24/7 Service)  │    │  (Automation)   │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌──────────────────┐
                       │   Gmail API      │
                       │  (Email System)  │
                       └──────────────────┘

🏆 Enterprise Features

  • 24/7 Operation - Continuous automation
  • Scalable Architecture - Handle thousands of leads
  • Professional Monitoring - Real-time dashboards
  • Error Recovery - Automatic failure handling
  • Audit Compliance - Complete activity logging
  • Docker Deployment - Production-ready containerization

Ready to automate your lead nurturing? 🚀

This system is production-ready and can handle enterprise-scale email campaigns with full automation, monitoring, and 24/7 operation.

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📧 Contact

工具与能力

工具数量

0

资源数量

0

提示词数量

0

能力标签

邮件营销Python本地部署自动化潜在客户管理Gmail集成企业级

数量来自来源目录的标注,具体工具清单以官方文档为准。

典型使用场景

消息与邮件处理

把群消息、邮件接进助手,按规则汇总和回复。

工单与任务流转

在对话里建单、改状态,少切换系统。

团队信息同步

把会议纪要和文档更新同步到协作工具。

选择建议

值得选的地方

  • 只提供 0 个工具,职责集中,接入后不容易误操作。
  • 有现成的安装命令,不用自己找入口,照着跑就能起服务。
  • 公开说明写得比较完整,接入前能看清能力边界。

先想清楚的地方

  • stdio 方式要求本地装好 运行环境,换机器要重新装一遍。
  • 需要自备访问 Token,没有凭据没法直接试。
  • 公开摘要偏短,具体能力和限制要回到仓库确认。

更适合

  • 需要在办公协作场景里让模型直接调用外部能力的人。
  • 想先快速试一下、能接受在本地跑服务的人。

不太适合

  • 完全不想在本地装环境的人。
  • 暂时拿不到访问 Token的人。
  • 需要厂商级 SLA 和技术支持的生产环境。

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按同分类和工具数量挑出最接近的几条,字段以各自详情页为准。

常见问题

5 条

Gmail MCP Agent 怎么安装?

公开资料给出的安装命令是 pip install -r requirements.txt,运行环境为 未标注。装完后按客户端要求写进 MCP 配置,重启客户端生效。

Gmail MCP Agent 怎么在 Claude Desktop、Cursor 里配置?

它是stdio 本地进程,客户端配置里要写启动命令和参数,运行环境是 未标注;保存后完全退出客户端再打开,配置才会生效。

Gmail MCP Agent 收费吗?

本站只整理公开资料,不代收费用,也没有标注具体价格。是否收费取决于服务本身和背后的接口额度,正式使用前按官方说明确认。

Gmail MCP Agent 需要 API Key 吗?

公开资料标注的认证方式是访问 Token,也就是要先拿到凭据才能连上;凭据只填在自己本地的配置里,不要写进公开仓库。

Gmail MCP Agent 支持哪些客户端?

公开资料没有标注具体客户端;能配置本地 MCP 命令的客户端都能接,配置方式参照官方文档。

来源信息

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