# Harvard WHO Health MCP > Harvard WHO Health MCP provides direct access to the World Health Organization's global data observatory. It lets your agent pull time-series statistics on everything from life expectancy and maternal mortality rates to immunization coverage (like measles or polio) and non-communicable diseases such as diabetes and obesity. Use this for deep, evidence-based research across multiple countries. ## Overview - **Category:** data-analytics - **Price:** Free - **Tags:** global-health, health-indicators, statistics, epidemiology, population-data, policy-analysis ## Description Need reliable health metrics for a project? This MCP connects your agent directly to the WHO Global Health Observatory data. It’s where you go when you need hard numbers on global public health trends—not aggregated reports. You can search thousands of indicators and compare specific metrics, like per capita healthcare spending or workforce density, across multiple nations over time. If your project involves policy analysis, epidemiology, or academic research, this is the data source you'll use. When connecting through Vinkius, you get one access point to run complex comparisons, whether tracking HIV/AIDS prevalence year-over-year or analyzing water and sanitation coverage globally. Your AI client handles the query complexity; you just focus on the insights. ## Tools ### compare_countries Compares a chosen health indicator across different countries over the most recent 10 years. ### get_countries Provides a list of WHO member country names and their ISO codes for use in other queries. ### get_dimensions Returns metadata explaining how to filter or break down the health data for more granular analysis. ### get_health_expenditure Retrieves data showing a country's total healthcare spending, adjusted for purchasing power. ### get_health_workforce Gets density metrics on the health workforce, such as the number of nurses or physicians per 10,000 people. ### get_hiv_aids Tracks and retrieves the percentage of a population living with HIV/AIDS over time for resource planning. ### get_immunization Gathers immunization coverage rates for key diseases like measles, polio, or DTP3 across countries. ### get_indicator_data Pulls time-series data for any specific WHO indicator code that you provide, returning country, year, and value. ### get_life_expectancy Retrieves the fundamental life expectancy at birth metric, a primary measure of population health. ### get_malaria Provides estimated case numbers for malaria across different regions and years. ### get_maternal_health Measures the number of maternal deaths per 100,000 live births to assess reproductive health quality. ### get_mortality Gathers mortality data broken down by cause, including under-5 or non-communicable causes. ### get_ncd Retrieves prevalence and consumption data for non-communicable diseases like diabetes, obesity, and smoking. ### get_tuberculosis Tracks the incidence of tuberculosis globally, helping monitor progress toward elimination goals. ### get_water_sanitation Gets metrics on safe drinking water access, sanitation facilities, and basic hygiene practices. ### search_indicators Searches the entire catalog of over 1000 WHO indicators to find the correct code for your research topic. ## Prompt Examples **Prompt:** ``` Compare life expectancy between USA, Brazil, Japan, and Nigeria ``` **Response:** ``` I've compared life expectancy at birth across these four countries using WHO data, showing trends over the past decade. ``` **Prompt:** ``` Show diabetes prevalence data for India ``` **Response:** ``` I've retrieved WHO diabetes prevalence data for India, showing time-series trends in blood glucose levels and diabetes rates among adults. ``` **Prompt:** ``` Get measles immunization coverage for sub-Saharan African countries ``` **Response:** ``` I've retrieved measles immunization coverage from WHO for sub-Saharan African countries, showing DTP3 and MCV1 coverage percentages and year-over-year progress. ``` ## Capabilities ### Compare multi-country trends It allows you to run time-series comparisons of a chosen health indicator across multiple countries simultaneously. ### Model mortality and demographic shifts You can pull detailed data on life expectancy, maternal deaths, and causes of death (like NCD or under-5 mortality). ### Track disease prevalence metrics The MCP retrieves historical data points for specific diseases, such as malaria cases, HIV/AIDS rates, or immunization coverage. ### Analyze resource allocation indicators It gathers data on country spending (health expenditure) and the local health workforce size per population. ### Search specific global metrics You can search for, and retrieve, specialized metrics like water sanitation coverage or non-communicable disease rates. ## Use Cases ### Assessing economic impact of disease. A policy analyst needs to know if a country's spending is keeping up with its health crisis. They prompt their agent: 'Compare Brazil and USA on both life expectancy using `get_life_expectancy` and healthcare expenditure using `get_health_expenditure`.' The MCP returns the comparative data set, allowing them to write an immediate policy brief. ### Building a regional immunization report. An academic researcher needs to show vaccination success across Africa. They use `get_immunization` for measles and polio coverage, then use the tool's ability to compare countries to visualize year-over-year progress in multiple sub-Saharan nations. ### Modeling environmental health risks. An epidemiologist is writing a report on water quality. They prompt their agent to pull data using `get_water_sanitation` and then correlate that with NCD rates from `get_ncd`, creating a comprehensive risk assessment. ### Reviewing pandemic preparedness. A public health consultant needs to compare infectious disease burdens. They use the agent to pull historical data on both `get_malaria` estimates and `get_tuberculosis` incidence for a target region, spotting critical trends before an outbreak. ## Benefits - Compare multiple nations at once. Instead of checking dashboards manually, the `compare_countries` tool lets your agent pull a side-by-side comparison of any indicator across dozens of countries in one go. - Track deep demographic shifts with confidence. Use `get_life_expectancy` or `get_maternal_health` to understand how population health has changed over decades, providing solid evidence for policy recommendations. - Analyze resource gaps quickly. Running the `get_health_workforce` tool lets you instantly compare doctor-to-population ratios between countries struggling with different issues. - Isolate specific disease data. Need to track progress on NCDs? The dedicated `get_ncd` tool gives you time-series stats on diabetes and obesity without needing complex code. - Scope out every metric needed. If you don't know the right indicator name, start with `search_indicators`. It lets your agent find the exact WHO code for what you need before pulling data. ## How It Works The bottom line is that your agent handles the complex API calls so you just get clean, comparable public health datasets. 1. First, tell your agent which indicator you need (e.g., 'life expectancy') and the countries involved. 2. Next, the MCP runs a query against the WHO data structure to pull specific years of time-series information for that metric. 3. Finally, you receive structured data containing country names, specific years, values, and confidence intervals ready for immediate analysis. ## Frequently Asked Questions **How do I find out what metrics the Harvard WHO Health MCP can track?** You use `search_indicators`. This tool searches the entire library of over 1000 indicators and gives you the specific code and name for everything from diabetes prevalence to water sanitation. **Can I compare life expectancy between different continents using Harvard WHO Health MCP?** Yes. You first use `get_countries` to find all relevant country codes, then run the `compare_countries` tool with those codes and 'life expectancy' as the indicator. **What data does get_health_workforce provide?** This tool provides density metrics, meaning it tells you how many types of health workers—like nurses or dentists—exist per 10,000 people in the country. **Is this MCP good for tracking NCDs? What tools should I use?** Absolutely. Use `get_ncd` to pull data on diabetes and obesity prevalence, or look at `get_mortality` for specific non-communicable death causes. **Can Harvard WHO Health MCP help me with historical data?** Yes. Most of the key tools, including `get_indicator_data`, are designed to pull time-series information, letting you track metrics across multiple years.