Smithery Ai COLUMBIA MCP SERVERS

This project is the deployment infrastructure and implementation of the Columbia University Model Context Protocol (MCP) servers, including core functions such as Docker containerized deployment, monitoring, high availability, and security.

Developer tools Virtualization #Containerized deployment #High availability #Monitoring system #Secure communication .TypeScript

rating : 2 points

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update time : 2025-04-29

What is an MCP server?

The MCP server is a standardized service framework developed by Columbia University for deploying and managing AI models, data processing tools, and other related services. It uses containerization technology to provide a highly available and scalable solution.

How to use the MCP server?

Services can be deployed and managed through simple command-line tools. The system provides complete monitoring and automated operation and maintenance functions.

Applicable scenarios

Suitable for research teams that need to quickly deploy AI services, enterprises that require scalable data processing capabilities, and development projects that need standardized service interfaces.

Main features

Containerized deploymentAll services use Docker containerization technology to ensure environment consistency and isolation.

Comprehensive monitoringIntegrated with Prometheus and Grafana to provide real-time performance monitoring and alarm functions.

Elastic scalingSupports horizontal scaling and can automatically adjust the number of service instances according to the load.

Security protectionBuilt-in SSL/TLS encryption, authentication, and access control mechanisms.

Advantages and limitations

Advantages

A complete out-of-the-box solution that reduces deployment time.

Modular design for easy expansion of new functions.

A complete monitoring and logging system for easy troubleshooting.

Supports multiple AI models and data services.

Limitations

Requires some knowledge of Docker and container technology.

Relatively high resource consumption.

Initial configuration is relatively complex.

How to use

Environment preparation

Ensure that Docker 20.10+ and Docker Compose 2.0+ are installed on the system.

Get the code

Clone the project repository to the local machine.

Configure the environment

Copy and modify the environment variable file.

Deploy services

Run the deployment script to start all services.

Usage examples

Deploy an AI model serviceDeploy a trained AI model as a callable API service.

Data preprocessing pipelineSet up an automated data preprocessing process.

Frequently asked questions

What if the deployment fails?

How to access the monitoring panel?

How to scale the services?

Related resources

Deployment guide

Detailed deployment steps and configuration instructions.

GitHub repository

Project source code and the latest updates.

Docker documentation

Official Docker documentation.

🚀 Columbia MCP Server

This repository contains the deployment infrastructure and server implementation of the Columbia Model Context Protocol (MCP) server.

🚀 Quick Start

1. Clone the repository

git clone https://github.com/GitDakky/COLUMBIA-MCP-SERVERS.git
cd COLUMBIA-MCP-SERVERS

2. Run the setup script

./docker/scripts/setup.sh

3. Configure environment variables

cp docker/.env.example .env
# Edit the .env file for configuration

4. Deploy to the production environment

./docker/scripts/deploy.sh

5. Monitor the deployment

./docker/scripts/monitor.sh

✨ Features

Docker-based deployment: Containerize services using Docker Compose.
High availability: Load balancing and svc replication.
Monitoring: Integration with Prometheus and Grafana.
Security: SSL/TLS, authentication, and security configurations.
Scalability: Horizontal scaling capabilities.
Backup and recovery: Automated backup system and point-in-time recovery.

📦 Installation

Repository Structure

.
├── docker/                 # Docker deployment configuration
│   ├── scripts/           # Deployment and maintenance scripts
│   │   ├── setup.sh       # Initial environment setup
│   │   ├── deploy.sh      # Production deployment
│   │   ├── rollback.sh    # Disaster recovery
│   │   └── monitor.sh     # Health and metrics monitoring
│   ├── rules/             # Prometheus monitoring rules
│   ├── docker-compose.prod.yml
│   ├── nginx.conf         # Nginx reverse proxy configuration
│   ├── prometheus.yml     # Prometheus monitoring configuration
│   └── redis.conf         # Redis configuration
├── docs/                  # Documentation
│   ├── DEPLOYMENT.md      # Detailed deployment guide
│   ├── MIGRATION_DASHBOARD.md
│   └── MIGRATION_PLAN.md
├── packages/              # Core packages
│   ├── core/             # Core functionality
│   ├── integrations/     # External service integrations
│   ├── platforms/        # Platform-specific implementations
│   └── utils/            # Shared tools
├── services/             # MCP service implementations
│   ├── ai/              # AI-related services
│   ├── data/            # Data services
│   └── tools/           # Tool services
└── shared/              # Shared configurations and types
    ├── config/          # Configuration management
    └── types/           # TypeScript type definitions