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Weather MCP Server

Weather MCP Server

by codewith1984
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

🌦️ Weather MCP Server Demo

A Model Context Protocol (MCP) compatible server that provides weather information using Ollama's LLM capabilities. This server exposes a get-weather tool that can be used by MCP clients to retrieve weather information for any city.

🛠 Prerequisites

  • Node.js v18+

  • Ollama installed and running locally

  • Ollama model: llama3 (or configure your preferred model)

Related MCP server: Weather MCP

🚀 Complete Setup & Installation

✅ Step 1: Clone and Install

git clone https://github.com/codewith1984/weather-mcp-server-typescript.git
cd weather-mcp-server
npm install

⚙️ Step 2: Setup Environment

# Copy environment template
cp .env.example .env

Edit the .env file to contain:

OLLAMA_API_URL=http://localhost:11434/api/generate
OLLAMA_MODEL=llama3

🤖 Step 3: Install and Setup Ollama

# Install Ollama (if not already installed)
# Visit https://ollama.com/ for installation instructions

# Start Ollama service
ollama serve

# In another terminal, pull the model
ollama pull llama3

# Verify Ollama is working
curl http://localhost:11434/api/version

Step 4: Test Ollama Connection

# Test if Ollama can generate responses
curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt": "Hello world",
  "stream": false
}'

Step 5: Run Diagnostics

# Run diagnostic to check if everything is working
npm run diagnose

🎯 Running the MCP Server

Start the Server

npm start

Expected output:

🚀 MCP Weather Server starting... 📡 Ollama URL: http://localhost:11434/api/generate 🤖 Model: llama3 ✅ MCP Server connected and ready!

🔍 Testing with MCP Inspector

Method 1: CLI Inspector

# Install MCP Inspector globally
npm install -g @modelcontextprotocol/inspector

# Run inspector
mcp-inspector

# Follow the web interface instructions

⚙️ Configuration

Environment Variables (.env file)

OLLAMA_API_URL=http://localhost:11434/api/generate
OLLAMA_MODEL=llama3

Performance Tuning

The server is optimized for quick responses:

15-second timeout for HTTP requests Aggressive Ollama parameters for faster generation Fallback from HTTP API to CLI if needed

##🔧 Troubleshooting

Quick Diagnosis

# Run the diagnostic script
npm run diagnose

Common Issues & Solutions

Issue: "Request timed out" errors

# Check if Ollama is running
ps aux | grep ollama

# Start Ollama if not running
ollama serve

# Check if model is available
ollama list | grep llama3

# If model not found, pull it
ollama pull llama3

Issue: "Request timed out" errors

# Check if Ollama is running
ps aux | grep ollama

# Start Ollama if not running
ollama serve

# Check if model is available
ollama list | grep llama3

# If model not found, pull it
ollama pull llama3

Issue: "Model not found" errors

# List available models
ollama list

# Pull the required model
ollama pull llama3

# Or try a smaller model for faster responses
ollama pull llama3:8b

Issue: Connection errors

# Verify Ollama is accessible
curl http://localhost:11434/api/version

# Check if port 11434 is open
netstat -an | grep 11434

# Restart Ollama service
pkill ollama
ollama serve

Issue: MCP Inspector connection fails

# Make sure your server is running
npm start

# Check the working directory path is correct
pwd

# Verify tsx is available
npm list tsx

Performance Tips

# Use a smaller model for faster responses
ollama pull llama3:8b

# Update .env to use the smaller model
echo "OLLAMA_MODEL=llama3:8b" >> .env

# Monitor system resources
top -p $(pgrep ollama)

📁 Project Structure

weather-mcp-server/
├── .env                # Environment configuration
├── .env.example       # Environment template
├── .gitignore         # Git ignore rules
├── main.ts            # MCP server implementation
├── ollamaClient.ts    # Ollama API client
├── diagnose.ts        # Diagnostic tool
├── package.json       # Dependencies and scripts
├── tsconfig.json      # TypeScript configuration
└── README.md          # This file

✅ Success Checklist

Complete this checklist to ensure everything is working:

Node.js v18+ installed Ollama installed and running (ollama serve) Model downloaded (ollama pull llama3) Project dependencies installed (npm install) Environment configured (.env file exists) Diagnostic passes (npm run diagnose) MCP server starts successfully (npm start) MCP Inspector connects successfully Weather tool responds to test quer

🚀 Quick Start Commands

# Complete setup in one go
git clone https://github.com/your-username/weather-mcp-server.git
cd weather-mcp-server
npm install
cp .env.example .env
ollama serve &
ollama pull llama3
npm run diagnose
npm start

🤝 Contributing

  1. Fork the repository

  2. Create a feature branch: git checkout -b feature-name

  3. Make your changes

  4. Test with MCP Inspector

  5. Submit a pull request

Available Tools

1 tool
get-weatherC

Tool to get the weather for a given city

Inputs: city (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as getting weather for a city, making it distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'get-weather' follows a clear verb-noun pattern, which is appropriate for its function.

    Tool Count2/5

    One tool is too few for a weather server's apparent scope, which typically involves multiple operations like forecasts, alerts, or historical data. This minimal set feels incomplete and may limit agent functionality.

    Completeness1/5

    The tool surface is severely incomplete for a weather domain. It only provides current weather for a city, lacking essential operations such as forecasts, alerts, historical data, or multi-location queries, which will likely cause agent failures.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

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