cncf-tech-advisor-mcp

cncf-tech-advisor-mcp

Enables querying the CNCF landscape to search for projects, get detailed information, GitHub metrics, maturity status, and case studies for technology decision support.

Category
访问服务器

README

CNCF Tech Advisor MCP Server

MCP Server for CNCF Landscape Technology Data. Access 2,398+ CNCF projects, GitHub metrics, maturity status, and case studies for technology decision support.

✨ Quick Start

# Install and run (no Java required!)
npx mcp-mcp-mcp-cncf-tech-advisor@latest

# With HTTP transport (for MCP Inspector)
npx mcp-mcp-mcp-cncf-tech-advisor@latest --port 8080

🚀 Installation

Option 1: NPM (Recommended) - No Java Required! 📦

Claude Desktop / Claude Code Integration

Add to ~/.claude/settings.json (Claude Code) or your MCP client config:

{
  "mcpServers": {
    "mcp-cncf-tech-advisor": {
      "command": "npx",
      "args": ["-y", "mcp-mcp-mcp-cncf-tech-advisor@latest"]
    }
  }
}

VS Code / Cursor / Windsurf

{
  "mcpServers": {
    "mcp-cncf-tech-advisor": {
      "command": "npx",
      "args": ["-y", "mcp-mcp-mcp-cncf-tech-advisor@latest"]
    }
  }
}

Global Installation

npm install -g mcp-mcp-cncf-tech-advisor

# Start the MCP server
mcp-mcp-cncf-tech-advisor

# HTTP mode for testing
mcp-mcp-cncf-tech-advisor --port 8080

Option 2: Docker (Production) 🐳

# Pull and run native image
docker run -i --rm -p 8080:8080 ghcr.io/jeanlopezxyz/mcp-cncf-tech-advisor:latest

# MCP STDIO usage
docker run -i --rm ghcr.io/jeanlopezxyz/mcp-cncf-tech-advisor:latest \
  -Dquarkus.mcp.server.stdio.enabled=true

Option 3: Build from Source (Development) 🔧

git clone https://github.com/jeanlopezxyz/mcp-cncf-tech-advisor-mcp.git
cd mcp-cncf-tech-advisor-mcp

# Build native binary
./mvnw package -DskipTests -Dnative

# Run
./target/*-runner

🚢 Deployment Scripts

Automated Deployment

The project includes automated deployment scripts for different platforms:

Docker Deployment

# Build and push to registry
./scripts/deploy.sh -p docker --push

# Development deployment
./scripts/deploy.sh -p docker -e dev --dev

# Native build with push
./scripts/deploy.sh -p docker --native --push

Kubernetes Deployment

# Deploy to staging
./scripts/deploy.sh -p k8s -e staging

# Deploy to production
./scripts/deploy.sh -p k8s -e production

NPM Deployment

# Publish to NPM registry
./scripts/deploy.sh -p npm --push

# Publish with custom tag
./scripts/deploy.sh -p npm --tag beta

Configuration Options

Environment Variables

# Java options
export JAVA_OPTS="-Xmx1g -Xms512m"

# MCP transport configuration
export STDIO_ENABLED=true
export HTTP_ENABLED=true

# Application settings
export QUARKUS_HTTP_PORT=8080
export QUARKUS_LOG_LEVEL=INFO

Application Properties

# application.properties
quarkus.mcp.server.stdio.enabled=true
quarkus.mcp.server.http.root-path=/mcp
quarkus.http.cors.enabled=true
quarkus.http.cors.origins=*

☸️ Kubernetes Deployment

Kubernetes Manifests

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-cncf-tech-advisor
spec:
  replicas: 3
  selector:
    matchLabels:
      app: mcp-cncf-tech-advisor
  template:
    metadata:
      labels:
        app: mcp-cncf-tech-advisor
    spec:
      containers:
      - name: mcp-cncf-tech-advisor
        image: ghcr.io/jeanlopezxyz/mcp-cncf-tech-advisor-mcp:latest
        ports:
        - containerPort: 8080
        env:
        - name: QUARKUS_MCP_SERVER_STDIO_ENABLED
          value: "false"
        - name: QUARKUS_MCP_SERVER_HTTP_ROOT_PATH
          value: "/mcp"
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /q/health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /q/health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: mcp-cncf-tech-advisor
spec:
  selector:
    app: mcp-cncf-tech-advisor
  ports:
  - port: 8080
    targetPort: 8080
  type: ClusterIP

Deploy with Helm (Optional)

# Add Helm repository
helm repo add mcp-cncf-tech-advisor https://charts.jeanlopez.tech
helm repo update

# Install chart
helm install mcp-cncf-tech-advisor mcp-cncf-tech-advisor/mcp-cncf-tech-advisor \
  --set image.tag=latest \
  --set replicas=3 \
  --set resources.requests.memory=512Mi

🔄 CI/CD Pipeline

The project includes a comprehensive CI/CD pipeline using GitHub Actions:

Automated Workflows

  • Testing: Multi-platform tests (Linux, macOS, Windows)
  • Building: JVM and native builds
  • Scanning: Security vulnerability scanning with Trivy
  • Deployment: Docker image publishing, NPM publishing
  • Integration: End-to-end MCP protocol testing

Build Triggers

  • Push to main/develop branches
  • Pull requests
  • Release events

Manual Testing

# Run full test suite
./scripts/test.sh

# Test MCP protocol
docker/scripts/mcp-test.sh

# Health check
curl -f http://localhost:8080/q/health

🔧 Configuration

Environment Variables

  • QUARKUS_MCP_SERVER_STDIO_ENABLED=true - Enable stdio transport (default for CLI)
  • QUARKUS_MCP_SERVER_HTTP_ROOT_PATH=/mcp - HTTP endpoint path
  • QUARKUS_HTTP_PORT=8080 - HTTP server port
  • CNCF_ADVISOR_LOG_LEVEL=INFO - Log level (DEBUG, INFO, WARN, ERROR)

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "cncf-tech-advisor": {
      "command": "mcp-cncf-tech-advisor"
    }
  }
}

🛠️ Available Tools (8)

Project Search & Analysis

  • searchProjects - Search CNCF projects by keyword, category, or maturity level
  • getProjectDetails - Get detailed information about a specific CNCF project
  • getProjectMetrics - GitHub metrics and community statistics for projects
  • getProjectMaturity - CNCF maturity status and progression timeline
  • getProjectsByCategory - List all projects in a specific category

Case Studies & Real-World Examples

  • searchCaseStudies - Search for CNCF case studies and end-user examples
  • getCaseStudiesByProject - Get case studies that use a specific project
  • getAllCaseStudies - Get all available CNCF case studies with filtering

✨ Features

  • O(1) Search: Ultra-fast indexing of 2,398+ CNCF projects
  • Live Data: Automatic updates from CNCF Landscape API
  • Smart Scoring: Relevance and popularity algorithms
  • Comprehensive: Project details, GitHub metrics, maturity status
  • Real-world Examples: Case studies and end-user implementations
  • Multi-platform Support: macOS (ARM64/x64), Linux (x64), Windows (x64)
  • No Java Dependencies: Native binaries for production deployment

💡 Example Prompts

Project Discovery

"Search for observability technologies in CNCF"
"Show me graduated projects related to service mesh"
"Find container runtime projects"
"List all projects in the serverless category"

Project Analysis

"Compare Prometheus and Grafana metrics"
"What's the maturity status of Istio?"
"Show me GitHub metrics for Kubernetes"
"Find popular container orchestration tools"

Case Studies & Real-World Examples

"Find case studies using Kubernetes"
"Search for microservices case studies"
"Show me end-user implementations of monitoring solutions"
"Get case studies about service mesh in production"

Technology Decision Support

"What are the best CNCF projects for logging?"
"Recommend projects for API gateway needs"
"Show me mature networking projects"
"Find projects with high community activity"

## 🏗️ Architecture

mcp-cncf-tech-advisor-mcp/ ├── src/main/java/io/mcp/cncf/ │ ├── analyzer/ # Analysis and recommendation engine │ ├── client/ # CNCF API integration │ ├── config/ # Configuration classes │ ├── model/ # Data models and records │ ├── prompt/ # MCP prompt templates │ └── tool/ # MCP tool implementations ├── src/main/resources/ │ └── application.properties ├── src/test/java/ ├── npm/ # NPM wrapper for easy distribution └── Dockerfile # Multi-stage native build


## 🔧 Development

### Prerequisites

- Java 25+ (for development/builder)
- Maven 3.9+
- Docker (optional, for native build)

### Building

```bash
# Build JAR
./mvnw package

# Build native executable
./mvnw package -Dnative

# Run tests
./mvnw test

# Run in dev mode
./mvnw quarkus:dev

Testing the MCP Server

# Start with stdio transport
java -jar target/mcp-cncf-tech-advisor-mcp-1.0.0-runner.jar \
  -Dquarkus.mcp.server.stdio.enabled=true

# Or start with HTTP transport
java -jar target/mcp-cncf-tech-advisor-mcp-1.0.0-runner.jar
# MCP endpoint: http://localhost:8080/mcp
# SSE endpoint: http://localhost:8080/mcp/sse

📊 Supported Technology Categories

  • Orchestration - Kubernetes, Docker, etc.
  • Runtime - Container runtimes, serverless platforms
  • Provisioning - Infrastructure as code, cloud provisioning
  • Observability - Monitoring, logging, tracing
  • Service Discovery - Service discovery and configuration
  • Service Mesh - Service mesh implementations
  • Networking - CNI, load balancers, ingress
  • Security - Authentication, authorization, secrets
  • Database - Databases and data stores
  • Storage - Persistent storage solutions
  • Streaming - Message queues and streaming platforms
  • Serverless - FaaS and serverless platforms
  • Integration - Integration and messaging
  • Policy - Policy as code, authorization
  • Artifact Management - Artifact repositories and registries

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

Development Process

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

  • CNCF for the amazing cloud-native ecosystem
  • Quarkus for the supersonic subatomic Java framework
  • MCP for the Model Context Protocol

📞 Support

🌟 Star History

Star History Chart


Made with ❤️ by Jean Lopez

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选