MCP.so
ログイン

Columbia MCP Servers

@MCP-Mirror

基本情報

カテゴリ

その他

トランスポート

stdio

作者

MCP-Mirror

Columbia MCP Servers について

Mirror of

Columbia MCP Servers の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

Columbia MCP Servers のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Columbia MCP Servers?

This repository provides the deployment infrastructure and server implementations for Columbia's Model Context Protocol (MCP) servers. It includes Docker‑based deployment, monitoring, and security tooling for a collection of AI, data, and tool services.

How to use Columbia MCP Servers?

Clone the repository, run the setup script (setup.sh), configure the .env file, and then deploy with deploy.sh. Monitoring can be started via monitor.sh. The server is designed for production use with containerized services.

Key features of Columbia MCP Servers

  • Docker‑based deployment with Docker Compose
  • High availability via load balancing and replication
  • Prometheus and Grafana monitoring integration
  • SSL/TLS, authentication, and rate limiting security
  • Horizontal scaling capabilities
  • Automated backup with point‑in‑time recovery

Use cases of Columbia MCP Servers

  • Deploying MCP services in a production environment
  • Monitoring service health and metrics with Prometheus and Grafana
  • Managing AI, data, and tool services as a unified stack
  • Implementing a secure, scalable MCP server infrastructure
  • Automating backup and disaster recovery

よくある質問

1

What are the system requirements?

Docker 20.10+, Docker Compose 2.0+, Node.js 18+, and Redis 6+ are required.

2

How do I deploy Columbia MCP Servers?

Clone the repository, run `setup.sh`, edit the `.env` file, and then execute `deploy.sh`. Use `monitor.sh` to check health and metrics.

3

What monitoring tools are included?

Prometheus (port 9090) and Grafana (port 3000) are integrated for metrics collection and visualization.

4

How is security implemented?

Services run in isolated containers with SSL/TLS encryption, Redis password protection, rate limiting, and regular security updates.

5

What types of services are provided?

The repository includes implementations for AI‑related services, data services, and tool services under the `services/` directory.

コメント

「その他」の他のコンテンツ