analytics-data

Malaysia Open Data

hithereiamaliff

by hithereiamaliff

Discover Malaysia Open Data: AI-first access, intelligent search, and seamless integration for Malaysia's leading open d

A unified AI-first bridge to Malaysia's open data ecosystem with intelligent search, context-aware geocoding, and comprehensive documentation for seamless AI integration.

github stars

2

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Real-time access to official Malaysian government dataIntelligent search with synonym expansion and fuzzy matchingMulti-provider geocoding optimized for Malaysia

best for

  • / Researchers analyzing Malaysian government statistics
  • / Developers building apps with Malaysian location data
  • / Data scientists working with Southeast Asian datasets
  • / Applications requiring Malaysian weather or transport data

capabilities

  • / Search Malaysian government datasets with intelligent query handling
  • / Parse Parquet data files directly with support for up to 500 rows
  • / Geocode Malaysian addresses using multiple providers (Google Maps, GrabMaps, OpenStreetMap)
  • / Access real-time weather forecasts and warnings
  • / Retrieve public transport and GTFS data
  • / Browse interactive data visualization dashboards

what it does

Provides AI-friendly access to Malaysia's government open datasets through unified search, data parsing, and geocoding capabilities. Connects to official Malaysian government data sources including statistics, weather, and transport data.

about

Malaysia Open Data is a community-built MCP server published by hithereiamaliff that provides AI assistants with tools and capabilities via the Model Context Protocol. Discover Malaysia Open Data: AI-first access, intelligent search, and seamless integration for Malaysia's leading open d It is categorized under analytics data.

how to install

You can install Malaysia Open Data in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Malaysia Open Data is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Malaysia Open Data MCP

MCP Endpoint: https://mcp.techmavie.digital/datagovmy/mcp

Analytics Dashboard: https://mcp.techmavie.digital/datagovmy/analytics/dashboard

MCP (Model Context Protocol) server for Malaysia's Open Data APIs, providing easy access to government datasets and collections.

Do note that this is NOT an official MCP server by the Government of Malaysia or anyone from Malaysia's Open Data/Jabatan Digital Negara/Ministry of Digital team.

Features

  • Enhanced Unified Search with flexible tokenization and synonym expansion
    • Intelligent query handling with term normalization
    • Support for plurals and common prefixes (e.g., "e" in "epayment")
    • Smart prioritization for different data types
  • Parquet File Support using pure JavaScript
    • Parse Parquet files directly in the browser or Node.js
    • Support for BROTLI compression
    • Intelligent date field handling for empty date objects
    • Increased row limits (up to 500 rows) for comprehensive data retrieval
    • Fallback to metadata estimation when parsing fails
    • Automatic dashboard URL mapping for visualization
  • Live Data Access Architecture
    • Real-time index fetching from GitHub (data-gov-my/datagovmy-meta)
    • In-memory caching with configurable TTL
    • Dynamic API calls for detailed metadata
  • Multi-Provider Geocoding
    • Support for Google Maps, GrabMaps, and Nominatim (OpenStreetMap)
    • Intelligent service selection based on location and available API keys
    • GrabMaps optimization for locations in Malaysia
    • Automatic fallback between providers
  • Comprehensive Data Sources
    • Malaysia's Data Catalogue with rich metadata
    • Interactive Dashboards for data visualization
    • Department of Statistics Malaysia (DOSM) data
    • Weather forecast and warnings
    • Public transport and GTFS data
  • Multi-Provider Malaysian Geocoding
    • Optimized for Malaysian addresses and locations
    • Three-tier geocoding system: GrabMaps, Google Maps, and Nominatim
    • Prioritizes local knowledge with GrabMaps for better Malaysian coverage
    • Automatic fallback to Nominatim when no API keys are provided

Architecture

This MCP server fetches dataset and dashboard metadata live from the data-gov-my/datagovmy-meta GitHub repository:

  • Live GitHub Indexes — Fetches all dataset and dashboard metadata via the GitHub Trees API and raw content URLs
  • Cache Pre-Warming — Indexes are fetched immediately on server startup, so the first user request is fast
  • In-Memory Caching — Indexes are cached in memory with a configurable TTL (default: 1 hour)
  • Background Refresh — When cache expires, stale data is served instantly while a background refresh fetches updated indexes. Users never experience fetch delays after the initial startup.
  • Dynamic Detail Fetching — Individual dataset/dashboard details are fetched on-demand from GitHub raw content

This approach provides several benefits:

  • Always up-to-date with the latest datasets and dashboards
  • No static data that goes stale
  • Zero-latency responses (pre-warmed cache + background refresh)
  • Consistent data access patterns

Documentation

  • TOOLS.md - Detailed information about available tools and best practices
  • PROMPT.md - AI integration guidelines and usage patterns

AI Integration

When integrating this MCP server with AI models:

  1. Use the unified search tool first - Always start with search_all for any data queries
  2. Follow the correct URL patterns - Use https://data.gov.my/... and https://open.dosm.gov.my/...
  3. Leverage Parquet file tools - Use parse_parquet_file to access data directly or get_parquet_info for metadata
  4. Live indexes - Dataset and dashboard indexes are fetched live from GitHub and cached in memory
  5. Consider dashboard visualization - For complex data, use the dashboard links provided by find_dashboard_for_parquet
  6. Leverage the multi-provider Malaysian geocoding - For Malaysian location queries, the system automatically selects the best provider (GrabMaps, Google Maps, or Nominatim) with fallback to Nominatim when no API keys are configured

Refer to PROMPT.md for comprehensive AI integration guidelines.

Installation

npm install

Quick Start (Hosted Server)

The easiest way to use this MCP server is via the hosted endpoint. No installation required!

Server URL:

https://mcp.techmavie.digital/datagovmy/mcp

Using Your Own API Keys

You can provide your own API keys via URL query parameters:

https://mcp.techmavie.digital/datagovmy/mcp?googleMapsApiKey=YOUR_KEY

Or via headers:

  • X-Google-Maps-Api-Key: YOUR_KEY
  • X-GrabMaps-Api-Key: YOUR_KEY
  • X-AWS-Access-Key-Id: YOUR_KEY
  • X-AWS-Secret-Access-Key: YOUR_KEY
  • X-AWS-Region: ap-southeast-5

Supported Query Parameters:

ParameterDescription
googleMapsApiKeyGoogle Maps API key for geocoding
grabMapsApiKeyGrabMaps API key for Southeast Asia geocoding
awsAccessKeyIdAWS Access Key ID for AWS Location Service
awsSecretAccessKeyAWS Secret Access Key
awsRegionAWS Region (default: ap-southeast-5)

⚠️ Important: GrabMaps Requirements

To use GrabMaps geocoding, you need ALL FOUR parameters:

  • grabMapsApiKey
  • awsAccessKeyId
  • awsSecretAccessKey
  • awsRegion

GrabMaps uses AWS Location Service under the hood, so AWS credentials are required alongside the GrabMaps API key.

Client Configuration

For Claude Desktop / Cursor / Windsurf, add to your MCP configuration:

{
  "mcpServers": {
    "malaysia-opendata": {
      "transport": "streamable-http",
      "url": "https://mcp.techmavie.digital/datagovmy/mcp"
    }
  }
}

With your own API key:

{
  "mcpServers": {
    "malaysia-opendata": {
      "transport": "streamable-http",
      "url": "https://mcp.techmavie.digital/datagovmy/mcp?googleMapsApiKey=YOUR_KEY"
    }
  }
}

Self-Hosted (VPS)

If you prefer to run your own instance, see deploy/DEPLOYMENT.md for detailed VPS deployment instructions with Docker and Nginx.

Analytics Dashboard

The hosted server includes a built-in analytics dashboard:

Dashboard URL: https://mcp.techmavie.digital/datagovmy/analytics/dashboard

Analytics Endpoints

EndpointDescription
/analyticsFull analytics summary (JSON)
/analytics/toolsDetailed tool usage stats (JSON)
/analytics/dashboardVisual dashboard with charts (HTML)

The dashboard tracks:

  • Total requests and tool calls
  • Tool usage distribution
  • Hourly request trends (last 24 hours)
  • Requests by endpoint
  • Top clients by user agent
  • Recent tool calls feed

Auto-refreshes every 30 seconds.

Available Tools

Unified Search

  • search_all: Primary search tool — searches across both datasets and dashboards with intelligent fallback and scoring

Data Catalogue

  • list_datasets_catalogue: Lists available datasets in the Data Catalogue
  • search_datasets_catalogue: Searches datasets in the Data Catalogue
  • filter_datasets_catalogue: Filters datasets by frequency, geography, demography, data source, or year range
  • get_dataset_details: Gets metadata/details for a specific dataset
  • get_dataset_filters: Gets available filter options for datasets

Dashboards

  • list_dashboards: Lists all available dashboards
  • search_dashboards: Searches dashboards by query
  • get_dashboard_details: Gets comprehensive metadata for a dashboard
  • get_dashboard_charts: Gets chart configurations for a specific dashboard

Department of Statistics Malaysia (DOSM)

  • list_dosm_datasets: Lists available datasets from DOSM
  • get_dosm_dataset: Gets data from a specific DOSM dataset

Parquet File Handling

  • parse_parquet_file: Parse and display data from a Parquet file URL
    • Supports up to 500 rows for comprehensive data analysis
    • Automatically handles empty date objects with appropriate formatting
    • Processes BigInt values for proper JSON serialization
  • get_parquet_info: Get metadata and structure information about a Parquet file
  • find_dashboard_for_parquet: Find the corresponding dashboard URL for a Parquet file

Weather

  • get_weather_forecast: Gets weather forecast for Malaysia
  • get_weather_warnings: Gets current weather warnings for Malaysia
  • get_earthquake_warnings: Gets earthquake warnings for Malaysia

Transport

  • list_transport_agencies: Lists available transport agencies with GTFS data
  • get_transport_data: Gets GTFS data for a specific transport agency

GTFS Parsing

  • parse_gtfs_static: Parses GTFS Static data (ZIP files with CSV data) for a specific transport provider
  • parse_gtfs_realtime: Parses GTFS Realtime data (Protocol Buffer format) for vehicle positions
  • get_transit_routes: Extracts route information from GTFS data
  • get_transit_stops: Extracts stop information from GTFS data, optionally filtered by route

Flood Warnings

  • get_flood_warnings: Gets current flood warnings for Malaysia, filterable by state, district, and severity

Test

  • hello: A simple test tool to verify that the MCP server is working correctly

Data-Catalogue Information Retrieval

The MCP server provides robust handling for data-catalogue information retrieval:

Date Handling in Parquet Files

  • Empty Date Objects: The system automatically detects and handles empty date objects in parquet files
  • Dataset-Specific Handling: Special handling for known datasets like employment_sector with annual data from 2001-2022
  • Pattern Recognition: Detects date patterns in existing data to maintain co

FAQ

What is the Malaysia Open Data MCP server?
Malaysia Open Data is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Malaysia Open Data?
This profile displays 57 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

List & Promote Your MCP Server

Share your MCP server with the developer community

GET_STARTED →
MCP server reviews

Ratings

4.757 reviews
  • Li Chawla· Dec 24, 2024

    We evaluated Malaysia Open Data against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ganesh Mohane· Dec 20, 2024

    We wired Malaysia Open Data into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Diya Patel· Dec 20, 2024

    Malaysia Open Data is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Diya Chen· Dec 16, 2024

    Malaysia Open Data is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Diya Li· Dec 12, 2024

    Malaysia Open Data reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Diya Gill· Nov 15, 2024

    Malaysia Open Data is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Rahul Santra· Nov 11, 2024

    Malaysia Open Data reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Liam Chawla· Nov 11, 2024

    I recommend Malaysia Open Data for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Li Bansal· Nov 11, 2024

    Strong directory entry: Malaysia Open Data surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Alexander Huang· Nov 7, 2024

    We evaluated Malaysia Open Data against two servers with overlapping tools; this profile had the clearer scope statement.

showing 1-10 of 57

1 / 6