World Bank Data360 MCP Server

Instructions

    This tool helps users discover commonly requested indicators without searching.
    Perfect for getting started or exploring what data is available.

    Categories included:
    - Demographics: Population, growth rate, density, fertility
    - Economy: GDP, GDP per capita, growth, inflation, trade
    - Health: Life expectancy, mortality rates (infant, under-5, maternal)
    - Education: Literacy rates, school enrollment
    - Labor: Unemployment, labor force participation, employment ratios
    - Poverty & Inequality: Poverty rates, Gini index, income distribution
    - Environment: Greenhouse gas emissions, forest area, renewable energy
    - Infrastructure: Electricity access, water, sanitation
    - Technology: Internet usage, mobile subscriptions, broadband

    Returns: List of 38 curated indicators with codes, names, descriptions, and categories.

    Usage: Browse the list, pick an indicator code, then use search_datasets
    to find the exact database ID before retrieving data.
    

Implementation Reference

• src/world_bank_mcp/server.py:529-579 (handler)

  Handler function for the 'list_popular_indicators' tool. Loads popular indicators from JSON, groups by category, and returns structured response. Registered via @server.tool() decorator.

  @server.tool()
  def list_popular_indicators() -> dict[str, Any]:
      """Get a curated list of popular World Bank indicators.
  
      This tool helps users discover commonly requested indicators without searching.
      Perfect for getting started or exploring what data is available.
  
      Categories included:
      - Demographics: Population, growth rate, density, fertility
      - Economy: GDP, GDP per capita, growth, inflation, trade
      - Health: Life expectancy, mortality rates (infant, under-5, maternal)
      - Education: Literacy rates, school enrollment
      - Labor: Unemployment, labor force participation, employment ratios
      - Poverty & Inequality: Poverty rates, Gini index, income distribution
      - Environment: Greenhouse gas emissions, forest area, renewable energy
      - Infrastructure: Electricity access, water, sanitation
      - Technology: Internet usage, mobile subscriptions, broadband
  
      Returns: List of 38 curated indicators with codes, names, descriptions, and categories.
  
      Usage: Browse the list, pick an indicator code, then use search_datasets
      to find the exact database ID before retrieving data.
      """
      indicators = load_popular_indicators()
  
      if not indicators:
          return {
              "success": False,
              "error": "Popular indicators file not found. Please ensure popular_indicators.json exists."
          }
  
      # Group by category for better organization
      by_category = {}
      for ind in indicators:
          category = ind.get("category", "Other")
          if category not in by_category:
              by_category[category] = []
          by_category[category].append({
              "code": ind["code"],
              "name": ind["name"],
              "description": ind["description"][:150] + "..." if len(ind["description"]) > 150 else ind["description"]
          })
  
      return {
          "success": True,
          "total_indicators": len(indicators),
          "categories": list(by_category.keys()),
          "indicators_by_category": by_category,
          "note": "These are the most commonly requested indicators. Use search_local_indicators for more specific searches."
      }

• src/world_bank_mcp/server.py:276-291 (helper)

  Helper function that loads and caches the popular indicators data from the local JSON file 'popular_indicators.json'.

  def load_popular_indicators() -> list[dict[str, Any]]:
      """Load curated popular indicators from JSON file (cached)"""
      global _popular_cache
  
      if _popular_cache is None:
          popular_path = Path(__file__).parent / "popular_indicators.json"
          try:
              with open(popular_path, 'r', encoding='utf-8') as f:
                  data = json.load(f)
                  _popular_cache = data["indicators"]
          except Exception as e:
              # Return empty list if file not found or error
              _popular_cache = []
  
      return _popular_cache