Which integrations are available for this server?

Supports containerization with multi-stage Docker builds for production deployment with health checks and security features Provides complete Kubernetes deployment manifests including deployment configurations, services, ingress controllers, and automated scaling with rolling updates

How do I use MCP Weather Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Weather Server what's the current temperature in Tokyo?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Weather Server

A streamable HTTP MCP (Model Context Protocol) server for getting weather data via the Open-Meteo API.

🚀 Features

Streamable HTTP Transport: Compatible with MCP 2025-03-26 specification
Session Management: Support for persistent sessions with unique IDs
Weather Tools: Three main tools for geolocation and weather data
REST API: Health endpoint for monitoring
Kubernetes Ready: Docker containerization and K8s deployment manifests
Local City Search: CSV-based local city database for offline search

📁 Project Structure

├── src/                           # Source code TypeScript
├── build/                         # Compiled JavaScript
├── static/                        # CSV city data files
├── test/                          # Test scripts and examples
│   ├── test-k8s-deployment.sh     # Kubernetes deployment tests
│   ├── test_mcp.js               # MCP functionality tests
│   ├── example-client.js         # Example MCP client
│   └── README.md                 # Test documentation
├── claude-configuration/          # Claude Desktop configuration
│   ├── claude_config*.json       # Various Claude configs
│   ├── install_claude_config.sh  # Auto-installation script
│   └── README.md                 # Configuration guide
├── k8s/                          # Kubernetes manifests
│   ├── deployment.yaml           # K8s deployment
│   ├── service.yaml              # K8s service
│   └── ingress.yaml              # K8s ingress
├── Dockerfile                    # Multi-stage Docker build
├── k8s-all-in-one.yaml          # Complete K8s deployment
├── build-deploy-container.sh                     # Automated deployment script
└── KUBERNETES_DEPLOYMENT.md     # Kubernetes deployment guide

📦 Requirement

cd flowise/kubernetes
MINIP=`minikube ip`
sed -i "s/CHANGEME/$MINIP/g" ingress-flowise.yaml
kubectl apply -f .

You need to import on flowise a new agent "Agentflows"

📦 Installation

# Clone and install dependencies
npm install

# Compile TypeScript
npm run build

# Start the server
npm start

🔧 Configuration

The server uses the following environment variables:

PORT: Listening port (default: 3000)

Example:

PORT=8080 npm start

🛠️ Available MCP Tools

1. `search_location`

Search for GPS coordinates of a city or location by name using the Open-Meteo geocoding API.

Parameters:

city_name (string): Name of the city or location to search for
country (string, optional): Country name to narrow the search
limit (number, optional): Maximum number of results to return (default: 5, max: 10)

Example response:

{
  "query": "Paris",
  "country_filter": null,
  "total_results": 5,
  "locations": [
    {
      "name": "Paris",
      "country": "France",
      "admin1": "Île-de-France",
      "latitude": 48.8566,
      "longitude": 2.3522,
      "timezone": "Europe/Paris",
      "population": 2161000,
      "elevation": 42
    },
    {
      "name": "Paris",
      "country": "United States",
      "admin1": "Texas",
      "latitude": 33.6609,
      "longitude": -95.5555,
      "timezone": "America/Chicago",
      "population": 25171,
      "elevation": 177
    }
  ]
}

2. `search_local_cities`

Search for cities in the local CSV database for offline functionality and faster local search.

Parameters:

city_name (string): Name of the city to search for (case insensitive partial match)
exact_match (boolean, optional): Whether to search for exact match or partial match (default: false)
limit (number, optional): Maximum number of results to return (default: 10, max: 50)

Example response:

{
  "query": "Paris",
  "exact_match": false,
  "total_found": 3,
  "results_returned": 3,
  "cities": [
    {
      "name": "Paris",
      "latitude": 48.8566,
      "longitude": 2.3522
    },
    {
      "name": "Paris-15e-Arrondissement",
      "latitude": 48.8421,
      "longitude": 2.3008
    },
    {
      "name": "Parisot",
      "latitude": 44.0625,
      "longitude": 1.7125
    }
  ]
}

3. `get_weather`

Get current weather information for a location.

Parameters:

latitude (number): Latitude in decimal degrees (-90 to 90)
longitude (number): Longitude in decimal degrees (-180 to 180)
location_name (string, optional): Optional location name for display

Example response:

{
  "location": "Paris",
  "weather": {
    "current_weather": {
      "temperature": 15.2,
      "windspeed": 10.5,
      "winddirection": 230,
      "weathercode": 1,
      "is_day": 1,
      "time": "2024-01-15T14:00"
    },
    "current_units": {
      "temperature": "°C",
      "windspeed": "km/h",
      "winddirection": "°",
      "weathercode": "wmo code",
      "is_day": "",
      "time": "iso8601"
    }
  },
  "lastUpdate": "2024-01-15T14:05:30.123Z"
}

4. `stream_weather`

Start streaming weather data with periodic updates.

Parameters:

latitude (number): Latitude in decimal degrees (-90 to 90)
longitude (number): Longitude in decimal degrees (-180 to 180)
location_name (string, optional): Optional location name for display
interval_seconds (number, default: 60): Update interval in seconds

🌐 HTTP Endpoints

Server Health

GET /health

Returns server status:

{
  "status": "OK",
  "timestamp": "2024-01-15T14:05:30.123Z"
}

MCP Endpoint

POST /mcp
GET /mcp  
DELETE /mcp

Main endpoint for MCP communications. Supports:

POST: Initialization and request sending
GET: SSE stream establishment for responses
DELETE: Session termination

📋 Usage Example with MCP Client

1. Session Initialization

const initRequest = {
  jsonrpc: '2.0',
  id: 1,
  method: 'initialize',
  params: {
    protocolVersion: '2024-11-05',
    capabilities: { tools: {} },
    clientInfo: { name: 'weather-client', version: '1.0.0' }
  }
};

const response = await fetch('http://localhost:8080/mcp', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Accept': 'application/json, text/event-stream'
  },
  body: JSON.stringify(initRequest)
});

const sessionId = response.headers.get('mcp-session-id');

2. List Available Tools

const listToolsRequest = {
  jsonrpc: '2.0',
  id: 2,
  method: 'tools/list',
  params: {}
};

const response = await fetch('http://localhost:8080/mcp', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Accept': 'application/json, text/event-stream',
    'mcp-session-id': sessionId
  },
  body: JSON.stringify(listToolsRequest)
});

3. Call Weather Tool

const weatherRequest = {
  jsonrpc: '2.0',
  id: 3,
  method: 'tools/call',
  params: {
    name: 'get_weather',
    arguments: {
      latitude: 48.8566,
      longitude: 2.3522,
      location_name: 'Paris'
    }
  }
};

const response = await fetch('http://localhost:8080/mcp', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Accept': 'application/json, text/event-stream',
    'mcp-session-id': sessionId
  },
  body: JSON.stringify(weatherRequest)
});

🧪 Testing

Quick Testing

# Start the server
PORT=8080 npm start

# Test the health endpoint
curl http://localhost:8080/health

Comprehensive Testing

The project includes various test scripts organized in the test/ directory:

# Run MCP functionality tests
cd test && node test_mcp.js

# Test local city search
cd test && node test_local_cities.js

# Test location search
cd test && node test_search_location.js

# Test Kubernetes deployment (requires K8s cluster)
./test/test-k8s-deployment.sh

For more testing options, see test/README.md.

🐳 Container Build & Kubernetes Deployment

This section describes how to containerize the MCP Weather Sample application and deploy it to a Kubernetes cluster.

📦 Containerization Overview

The application uses a multi-stage Docker build approach for optimal production deployment:

Build Stage: Compiles TypeScript source code
Production Stage: Creates minimal runtime image with only production dependencies
Security: Runs as non-root user with minimal privileges
Health Checks: Built-in container health monitoring

🏗️ Docker Build Process

Automated Build & Deployment

# Complete build and deployment pipeline
./build-deploy-container.sh all

# Individual steps
./build-deploy-container.sh build    # Build Docker image only
./build-deploy-container.sh deploy   # Deploy to Kubernetes only
./build-deploy-container.sh cleanup  # Remove deployment

Manual Docker Operations

# Build the Docker image
docker build -t mcp-weather-sample:latest .

# Run container locally for testing
docker run -p 8080:8080 mcp-weather-sample:latest

# Check container health
curl http://localhost:8080/health

# View container logs
docker logs <container-id>

Docker Image Details

Base Image: node:20-alpine (lightweight Linux distribution)
Final Size: ~150MB (optimized with multi-stage build)
Port: 8080 (configurable via PORT environment variable)
Health Check: Built-in HTTP health endpoint monitoring
User: Non-root user (nextjs:nodejs) for security

⚓ Kubernetes Deployment Architecture

Deployment Components

The Kubernetes deployment includes:

Deployment: 3 replicas with rolling updates
Service: ClusterIP service exposing port 80→8080
ConfigMap: Environment configuration management
Ingress: External traffic routing with CORS support
Health Probes: Startup, liveness, and readiness checks

Resource Configuration

resources:
  requests:
    memory: "128Mi"    # Minimum memory allocation
    cpu: "100m"        # Minimum CPU allocation (0.1 core)
  limits:
    memory: "512Mi"    # Maximum memory limit
    cpu: "500m"        # Maximum CPU limit (0.5 core)

High Availability Features

3 Replicas: Ensures service availability during pod failures
Rolling Updates: Zero-downtime deployments
Health Checks: Automatic pod restart on failure
Resource Limits: Prevents resource exhaustion
CORS Configuration: Proper header handling for MCP sessions

🚀 Deployment Options

Option 1: All-in-One Deployment

# Deploy everything with single command
kubectl apply -f k8s-all-in-one.yaml

# Monitor deployment progress
kubectl rollout status deployment/mcp-weather-app

# Check pod status
kubectl get pods -l app=mcp-weather-app

Option 2: Component-by-Component

# Deploy individual components
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml
kubectl apply -f k8s/ingress.yaml

Option 3: Automated Script

# Use the provided deployment script
./build-deploy-container.sh all

# Script features:
# - Validates kubectl connectivity
# - Builds Docker image
# - Applies Kubernetes manifests
# - Waits for deployment readiness
# - Provides access instructions

🔧 Configuration Management

Environment Variables

Configure the application through the ConfigMap:

apiVersion: v1
kind: ConfigMap
metadata:
  name: mcp-weather-config
data:
  NODE_ENV: "production"
  PORT: "8080"
  LOG_LEVEL: "info"

Ingress Configuration

# Custom domain configuration
spec:
  rules:
  - host: mcp-weather.your-domain.com  # Replace with your domain
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: mcp-weather-service
            port:
              number: 80

🧪 Testing Deployment

Quick Validation

# Test deployment health
./test/test-k8s-deployment.sh

# Manual health check via port-forward
kubectl port-forward service/mcp-weather-service 8080:8080
curl http://localhost:8080/health

# Debug
kubectl exec -it pods/$(kubectl get pods  --no-headers -o custom-columns=":metadata.name" | grep 'flowise'|head -1) sh

Comprehensive Testing

# Test all endpoints and functionality
./test/test-k8s-deployment.sh local

# Test via ingress (requires domain configuration)
./test/test-k8s-deployment.sh ingress your-domain.com

📊 Monitoring & Troubleshooting

Checking Deployment Status

# Pod status and logs
kubectl get pods -l app=mcp-weather-app
kubectl logs -l app=mcp-weather-app -f

# Service endpoints
kubectl get endpoints mcp-weather-service
kubectl describe service mcp-weather-service

# Ingress status
kubectl get ingress mcp-weather-ingress
kubectl describe ingress mcp-weather-ingress

Common Troubleshooting

# Pod not starting
kubectl describe pod <pod-name>
kubectl logs <pod-name> --previous

# Service not accessible
kubectl port-forward service/mcp-weather-service 8080:80
curl http://localhost:8080/health

# Image pull issues
kubectl describe pod <pod-name> | grep -A 5 "Events:"

🔄 Scaling & Updates

Horizontal Scaling

# Scale to 5 replicas
kubectl scale deployment mcp-weather-app --replicas=5

# Verify scaling
kubectl get pods -l app=mcp-weather-app

Rolling Updates

# Update image version
kubectl set image deployment/mcp-weather-app \
  mcp-weather-app=mcp-weather-sample:v2.0.0

# Monitor rollout
kubectl rollout status deployment/mcp-weather-app

# Rollback if needed
kubectl rollout undo deployment/mcp-weather-app

For complete deployment instructions and advanced configurations, see KUBERNETES_DEPLOYMENT.md.

⚙️ Claude Desktop Integration

The project includes pre-configured Claude Desktop configurations in the claude-configuration/ directory:

# Auto-install Claude configuration
cd claude-configuration && ./install_claude_config.sh

# Validate HTTP configuration
cd claude-configuration && ./validate_claude_http.sh

Available configurations:

Basic: claude_config.json - Minimal setup
Detailed: claude_config_detailed.json - Full options
HTTP: claude_config_http.json - HTTP transport specific

For detailed setup instructions, see claude-configuration/README.md.

🔍 Using MCP Inspector

You can use the official MCP Inspector to visually explore and test your weather server tools in a user-friendly interface.

Installation and Setup

Install MCP Inspector (requires Node.js):
```
npx @modelcontextprotocol/inspector
```
Start your weather server:
```
PORT=8080 npm start
```
Configure MCP Inspector:
- Server URL: http://localhost:8080/mcp
- Transport: HTTP (Streamable)
- Protocol Version: 2024-11-05

Visual Interface Features

MCP Inspector Example

The MCP Inspector provides:

🛠️ Tools Explorer: Browse all available tools (search_location, get_weather, stream_weather)
📝 Interactive Forms: Easy-to-use forms for entering tool parameters
🎯 Real-time Testing: Execute tools and see live responses
📊 Response Viewer: JSON-formatted results with syntax highlighting
🔄 Session Management: Visual session state and connection status
📈 Request/Response History: Track all your API interactions

Example Workflow

Connect to your server using the Inspector
Explore the three available weather tools
Test location search:
- Tool: search_location
- Parameters: city_name: "Paris"
Get weather data:
- Tool: get_weather
- Parameters: Use coordinates from previous search
Monitor streaming:
- Tool: stream_weather
- Watch real-time weather updates

Benefits

No coding required: Test your MCP server without writing client code
Visual debugging: See exactly what data flows between client and server
Tool validation: Verify parameter schemas and response formats
Rapid prototyping: Quickly iterate on your MCP server implementation

The Inspector is particularly useful during development to ensure your weather server works correctly before integrating it into larger applications.

🏗️ Architecture

Express.js: HTTP server
MCP SDK: MCP protocol implementation with StreamableHTTPServerTransport
Open-Meteo API: Weather data source
TypeScript: Development language with compilation to JavaScript

Integrate MCP server on flowise (lowcode GenAI tool)

On flowise initialise an agentflow
Retrieve MCP configuration through MCP inspector Copy/Paste form "Server Entry" on Inspector interface
Configure a "MCP Custom" tool and add exposed toolexposed by the MCP server on flowise add MCP configuration specifciation copied from Inspector

alt text

📄 Specifications

MCP Protocol: Version 2024-11-05 and 2025-03-26
Transport: Streamable HTTP with SSE support
Data Format: JSON-RPC 2.0
Weather API: Open-Meteo

🔗 Useful Links

📝 License

This project is a demonstration example based on open-meteo-mcp.

MCP Weather Server

🚀 Features

📁 Project Structure

📦 Requirement

📦 Installation

🔧 Configuration

🛠️ Available MCP Tools

1. search_location

2. search_local_cities

3. get_weather

4. stream_weather

🌐 HTTP Endpoints

Server Health

MCP Endpoint

📋 Usage Example with MCP Client

1. Session Initialization

2. List Available Tools

3. Call Weather Tool

🧪 Testing

Quick Testing

Comprehensive Testing

🐳 Container Build & Kubernetes Deployment

📦 Containerization Overview

🏗️ Docker Build Process

Automated Build & Deployment

Manual Docker Operations

Docker Image Details

⚓ Kubernetes Deployment Architecture

Deployment Components

Resource Configuration

High Availability Features

🚀 Deployment Options

Option 1: All-in-One Deployment

Option 2: Component-by-Component

Option 3: Automated Script

🔧 Configuration Management

Environment Variables

Ingress Configuration

🧪 Testing Deployment

Quick Validation

Comprehensive Testing

📊 Monitoring & Troubleshooting

Checking Deployment Status

Common Troubleshooting

🔄 Scaling & Updates

Horizontal Scaling

Rolling Updates

⚙️ Claude Desktop Integration

🔍 Using MCP Inspector

Installation and Setup

Visual Interface Features

Example Workflow

Benefits

🏗️ Architecture

Integrate MCP server on flowise (lowcode GenAI tool)

📄 Specifications

🔗 Useful Links

📝 License

Resources

Latest Blog Posts

MCP directory API

1. `search_location`

2. `search_local_cities`

3. `get_weather`

4. `stream_weather`