COLUMBIA MCP SERVERS

The deployment infrastructure and service implementation of Columbia University's MCP servers, including Docker containerized deployment, monitoring, security, and high - availability design.

Developer tools Artificial intelligence chatbots #Container deployment #Monitoring system #High availability #Data service .TypeScript

rating : 2 points

downloads : 13

update time : 2025-04-29

What are MCP servers?

MCP servers are an implementation of a standardized AI model interaction protocol developed by Columbia University. They provide unified API interfaces, security authentication, and monitoring functions, enabling different AI models to work together securely and efficiently.

How to use MCP servers?

The full - set of services, including AI model interfaces, data services, and monitoring systems, can be started through a simple Docker deployment. Developers can interact with the models through REST APIs or the web interface.

Use cases

Suitable for AI application development requiring multi - model collaboration, model service deployment, and enterprise AI solutions that need standardized interfaces.

Main features

Containerized deploymentUse Docker to provide an out - of - the - box deployment experience, with all services running in isolated containers.

High availabilityBuilt - in load balancing and service replication mechanisms ensure uninterrupted service operation.

Comprehensive monitoringIntegrate Prometheus and Grafana to provide real - time performance monitoring and alerting functions.

Enterprise - level securitySupport SSL/TLS encrypted communication, Redis password protection, and API access control.

Advantages and limitations

Advantages

Standardized interfaces simplify the integration of different AI models.

Out - of - the - box containerized deployment saves configuration time.

A comprehensive monitoring system facilitates operation and maintenance management.

Built - in security functions protect models and data.

Limitations

Requires a Docker environment, which is not very friendly to pure front - end developers.

Consumes a large amount of resources, and may be over - configured for small projects.

Has a steep learning curve and requires understanding of multiple components.

How to use

Environment preparation

Ensure that Docker 20.10+ and Docker Compose 2.0+ are installed.

Clone the repository

Get the latest MCP server code.

Configure the environment

Copy and edit the environment variable file.

Start the services

Use Docker Compose to start all services.

Usage examples

Multi - model dialogue systemUse language models and knowledge graph models simultaneously to answer complex questions.

Automated data processing pipelineUse data pre - processing models and analysis models in series.

Frequently Asked Questions

Which AI frameworks are supported by MCP servers?

How to add a custom model?

How long is the monitoring data retained?

Related resources

Deployment guide

Detailed deployment instructions for the production environment.

GitHub repository

Project source code and issue tracking.

MCP protocol specification

Official specification document of the Model Context Protocol.

🚀 Columbia MCP Servers

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

🚀 Quick Start

Clone the repository:

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

Run the setup script:

./docker/scripts/setup.sh

Configure environment variables:

cp docker/.env.example .env
# Edit the .env file to configure your settings

Deploy to the production environment:

./docker/scripts/deploy.sh

Monitor the deployment status:

./docker/scripts/monitor.sh

✨ Features

Docker-based Deployment: Containerized services using Docker Compose.
High Availability: Load balancing and service replication.
Monitoring: Integration with Prometheus and Grafana.
Security: SSL/TLS, authentication, and security configuration.
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/            # Common utilities
├── 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