ManaMurah MCP Server
A Model Context Protocol (MCP) server for Malaysian price data from KPDN Pricecatcher. This server enables direct integration with Claude Desktop and other MCP-compatible AI tools for querying Malaysian consumer goods prices.
Features
🇲🇾 Official Malaysian Price Data - KPDN Pricecatcher data via OpenDOSM
🤖 AI-Optimized - Natural language queries with intelligent parsing
⚡ Serverless - Deployed on Cloudflare Workers for global performance
🔒 Rate Limited - Built-in abuse protection and fair usage
📊 Rich Analytics - Price comparisons, trends, and market insights
🎯 Claude Desktop Ready - One-click setup for Claude Desktop integration
Live Demo
The MCP server is deployed and accessible at:
Quick Start
Deploy to Cloudflare Workers
# Clone or create from template
npm create cloudflare@latest manamurah-mcp-server --template=cloudflare/ai/demos/remote-mcp-authless
# Replace src/ contents with ManaMurah implementation
# (Copy files from this directory)
# Install dependencies
npm install
# Deploy to Cloudflare Workers
npm run deploy
Connect to Claude Desktop
- Get your deployed Workers URL (e.g.,
https://mcp.manamurah.com) - Add to Claude Desktop MCP configuration:
{
"manamurah": {
"command": "node",
"args": ["/path/to/mcp-client.js"],
"env": {
"MCP_SERVER_URL": "https://mcp.manamurah.com/sse"
}
}
}
- Restart Claude Desktop
- Start asking about Malaysian prices!
🔍 get_malaysian_prices
Search for current prices with natural language queries.
Examples:
- "rice prices in Kuala Lumpur"
- "cheapest chicken in Penang hypermarkets"
- "cooking oil under RM20 in Selangor"
📊 compare_prices
Compare prices across different regions or retail chains.
Examples:
- Compare rice prices between KL and Penang
- Find price differences across retail chains
- Regional price analysis for specific items
📈 analyze_price_trends
Analyze price trends and market patterns over time.
Examples:
- Price volatility analysis
- Seasonal price patterns
- Regional market trends
💡 get_market_insights
Get market intelligence and insights about price anomalies.
Examples:
- Recent price changes
- Market anomaly detection
- Regional price differences
Example Usage
Basic Price Search
User: "What are rice prices in Kuala Lumpur?"
MCP Response:
📊 Summary: Rice prices in Kuala Lumpur range from RM15.20-RM25.80 per 5kg
💡 Key Insights:
• Hypermarkets offer 18% lower prices than convenience stores
• Significant price variation exists across different retailers
📈 Price Statistics:
• Average: RM18.50
• Range: RM15.20 - RM25.80
• Price Variation: 32%
[Detailed price listings follow...]
Price Comparison
User: "Compare chicken prices between Penang and Johor"
MCP Response:
📊 Summary: Penang has lower average chicken prices (RM8.20) compared to Johor (RM9.10)
## Regional Comparison
### 1. Penang
• Average Price: RM8.20
• Price Range: RM7.50 - RM9.80
• Sample Size: 15 price points
### 2. Johor
• Average Price: RM9.10
• Price Range: RM8.20 - RM11.50
• Sample Size: 12 price points
💡 Comparison Insights:
• Most Affordable: Penang (RM8.20 average)
• Potential Savings: RM0.90 (9.9%) by choosing Penang
Development
Local Development
# Install dependencies
npm install
# Start development server
npm run dev
# Type checking
npm run type-check
# Linting
npm run lint
Project Structure
src/
├── index.ts # Main MCP server implementation
├── utils/
│ ├── api-client.ts # ManaMurah API client
│ ├── query-parser.ts # Natural language query parsing
│ ├── response-formatter.ts # MCP response formatting
│ └── rate-limiter.ts # Rate limiting implementation
└── types/
└── manamurah.ts # TypeScript type definitions
Configuration
Environment variables in wrangler.toml:
[vars]
MANAMURAH_API_BASE = "https://api.manamurah.com"
RATE_LIMIT_ENABLED = "true"
CACHE_TTL = "300"
MAX_QUERIES_PER_MINUTE = "10"
MAX_QUERIES_PER_HOUR = "100"
Rate Limits
- Per Minute: 10 requests
- Per Hour: 100 requests
- Automatic Cleanup: Old request data is cleaned up automatically
Rate limits help ensure fair usage and prevent abuse while allowing genuine research and analysis.
Features
Natural Language Processing
- Intelligent extraction of items, locations, and price constraints
- Support for Malaysian terms (e.g., "beras" for rice, "ayam" for chicken)
- Price range detection ("under RM20", "between RM10 and RM15")
- Location recognition for all Malaysian states and major cities
- Markdown-formatted responses optimized for Claude Desktop
- Statistical analysis with averages, ranges, and insights
- Suggested follow-up questions for continued exploration
- Data source attribution and freshness indicators
Error Handling
- User-friendly error messages with helpful suggestions
- Graceful degradation when data is unavailable
- Query improvement recommendations
- Comprehensive error logging for debugging
Data Source
Official Government Data: KPDN Pricecatcher program via OpenDOSM
- Daily data updates (subject to government publication schedules)
- Comprehensive coverage of Malaysian retail prices
- Data includes hypermarkets, supermarkets, convenience stores, and grocery shops
- Covers all Malaysian states and major urban centers
Support
Getting Help
License
MIT License - see LICENSE file for details.
Contributing
Contributions welcome! Please read our contributing guidelines and submit pull requests for any improvements.
Built with ❤️ for the Malaysian data community
Making Malaysian price data accessible to AI tools and researchers worldwide.