이 저장소는 모델 Context Protocol (MCP)의 구현을 포함, 외부 도구 및 데이터 소스와 AI 시스템을 연결하는 개방 표준.

점화AI에 관하여

LightingAI는 접근 및 협업 도구를 통해 AI 역량을 강화하는 데 전념하는 오픈 소스 조직입니다. 우리는 강력한 윤리적, 혁신적인 AI 솔루션을 만들 수있는 커뮤니티 중심 개발의 힘을 믿습니다.

저장소 내용

여러 MCP 서버 구현을 포함하는 monorepo입니다.

-azure-storage-mcp: Table Storage, Blob Storage, Service Bus Queues 및 Storage Queues와 Azure Storage 통합 완료

• azure-ai-search: Azure AI 검색 통합 (개발)
• linkedin-mcp: OAuth 인증과 LinkedIn API 통합
• linkedin-mcp-http: LinkedIn MCP 서버의 HTTP 변형
• fit-mcp: 피트니스 및 영양 계산을 위한 MCP 구현
• node-mcp: 간단한 Node.js MCP 구현
• ** mcp-template**: 새로운 MCP 서버를 만드는 기본 템플릿

개발

# 필수품

• Node.js 및 pnpm
• Service-specific credentials (개인 서버 문서 참조)

명령

MCP (Model Context Protocol)

This repository contains implementations of the Model Context Protocol (MCP), an open standard that connects AI systems with external tools and data sources.

About IgnitionAI

IgnitionAI is an open-source organization dedicated to advancing AI capabilities through accessible and collaborative tools. We believe in the power of community-driven development to create robust, ethical, and innovative AI solutions.

Repository Contents

This is a monorepo containing multiple MCP server implementations:

• azure-storage-mcp: Complete Azure Storage integration with Table Storage, Blob Storage, Service Bus Queues, and Storage Queues
• azure-ai-search: Azure AI Search integration (in development)
• linkedin-mcp: LinkedIn API integration with OAuth authentication
• linkedin-mcp-http: HTTP variant of LinkedIn MCP server
• fit-mcp: MCP implementation for fitness and nutrition calculations
• node-mcp: Simple Node.js MCP implementation
• mcp-template: Base template for creating new MCP servers

Development

Prerequisites

• Node.js and pnpm
• Service-specific credentials (see individual server documentation)

Commands

• pnpm build - Build all projects
• pnpm start - Start development server
• pnpm start:prod - Start production server
• pnpm inspect - Run MCP inspector for debugging
• docker compose up -d - Start LinkedIn HTTP server

About MCP

The Model Context Protocol (MCP) is a standard that enables AI models to interact with external tools, APIs, and services, extending their capabilities beyond their initial training. These repositories are available for everyone to use, modify, and contribute to.

Configuration Examples

Azure Storage MCP

"azure-storage": {
  "command": "npx",
  "args": ["azure-storage-mcp"],
  "env": {
    "AZURE_STORAGE_CONNECTION_STRING": "your-connection-string"
  }
}

Azure AI Search MCP

"azure-ai-search": {
  "command": "npx",
  "args": ["azure-ai-search-mcp"],
  "env": {
    "AZURE_SEARCH_ENDPOINT": "your-search-endpoint",
    "AZURE_SEARCH_API_KEY": "your-api-key"
  }
}

LinkedIn MCP

"linkedin": {
  "command": "npx",
  "args": ["-y", "linkedin-mcp"],
  "env": {
    "LINKEDIN_CLIENT_ID": "your-client-id",
    "LINKEDIN_CLIENT_SECRET": "your-client-secret",
    "LINKEDIN_REDIRECT_URI": "your-redirect-uri"
  }
}

Fit MCP

"fit-mcp": {
  "command": "npx",
  "args": ["fitmcp"]
}

How to Get LinkedIn API Keys

To obtain the necessary LinkedIn API credentials (LINKEDIN_CLIENT_ID, LINKEDIN_CLIENT_SECRET, and LINKEDIN_REDIRECT_URI), follow these steps:

1. Create a LinkedIn Developer Account
   Visit LinkedIn Developer Portal and log in with your LinkedIn account.

2. Create a New App
   Click Create app, fill out the required details, and submit.

3. Get Client ID and Client Secret