WHO GHO MCP for AI. Compare health metrics across nations and time.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
WHO GHO provides direct access to 2,200+ global health indicators from 194 countries, sourced straight from the World Health Organization.
You can pull time-series data on anything from life expectancy and child mortality to air pollution levels. Need a quick snapshot of Brazil's overall health metrics? Or do you need to track HIV/AIDS prevalence across multiple continents over two decades? This MCP handles it all by standardizing complex global datasets for your AI agent.
What your AI can do
Get who country profile
Pulls an immediate health snapshot for any country using its three-letter ISO code.
Get who indicator data
Fetches year-by-year data for a specific WHO indicator, filtering by country and available sex breakdown.
Search who indicators
Searches the entire 2,200+ index of global health indicators to find the necessary code for your query.
Instantly get a key metrics snapshot for any given nation, including life expectancy and obesity rates.
Find the specific WHO code you need to track an indicator, like 'malaria' or 'immunization coverage'.
Pull yearly time-series values for a selected health metric, filtered by country and sex.
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WHO GHO: Accessing Global Health Data with 3 Tools
Use these three tools to search indicator codes, pull instant country profiles, or retrieve detailed historical health data from the WHO.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using WHO GHO on VinkiusGet Who Country Profile
Pulls an immediate health snapshot for any country using its three-letter ISO code.
Get Who Indicator Data
Fetches year-by-year data for a specific WHO indicator, filtering by country and...
Search Who Indicators
Searches the entire 2,200+ index of global health indicators to find the necessary...
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The manual process of comparing global health data is a nightmare.
Right now, compiling a comparative report—say, tracking child mortality rates between Brazil, India, and Japan over the last twenty years—is painful. You jump from country website to country website, finding different metrics listed under slightly different names. You download multiple CSVs, then spend hours cleaning up date formats, dealing with missing data points, and making sure you're comparing apples to apples.
With this MCP, your agent handles the complexity entirely. You ask for 'child mortality rates in Brazil vs Japan since 2010.' The system runs `search_who_indicators` first, finds the correct global code, and then uses `get_who_indicator_data` to pull all those standardized time-series points into one clean output. You get the data ready for analysis instantly.
Get a comprehensive view with `get_who_country_profile`.
Before you even start tracking specific diseases, you have to know where you stand. Today, getting an initial health snapshot for a country means navigating complex government reports just to find the basic metrics: life expectancy, obesity prevalence, etc. It’s slow and disjointed.
Now, running `get_who_country_profile` gives you that full, standardized picture instantly. You get key indicators like healthy life expectancy right in your chat window. It's a massive time saver.
What your AI can actually do with this
This connector gives your AI client direct access to the WHO Global Health Observatory. It’s an authoritative source, meaning the data is vetted and standardized. Instead of manually cross-referencing reports or searching through disparate academic databases, you can ask your agent to build a complete picture of global health trends on demand.
Whether you need a high-level overview—like a quick profile for India—or deep time-series comparisons of indicators like tuberculosis incidence over 30 years, this MCP handles the complexity. It structures raw WHO data so your agent doesn't choke on inconsistent formats. When you connect via Vinkius, you get full visibility into every single data point called and how it contributes to the final answer.
This means nothing happens in the dark; you always know exactly what data flowed through to generate the insights.
019d7622-5b73-7257-9064-dfa25541c91c Here's how it actually works
The bottom line is you move from needing to know specific WHO codes to just asking your agent what data you need.
First, use the search function to find the correct indicator code. You'll need this code before you can pull specific data.
Next, run a profile check using an ISO-3 country code to get general health metrics for that nation.
Finally, input both the found indicator code and the country code to retrieve the full time-series dataset.
Who is this actually for?
Epidemiologists, public health researchers, and international development consultants. You're the person who spends hours manually compiling comparison tables across multiple countries for a grant proposal or policy brief.
Runs meta-analyses comparing outcomes—for example, tracking changes in child mortality rates between three different continents over two decades.
Compares national health spending metrics against indicators like healthy life expectancy to advise government bodies on resource allocation.
Tracks the prevalence and incidence of specific diseases, such as comparing current rates of malaria versus historical records for a developing nation.
What Changes When You Connect
Quickly assess a country's overall status with get_who_country_profile, getting key data like life expectancy, healthy life expectancy, and obesity prevalence in one go. You skip the initial report compilation step.
You don't have to guess what code to use; first run search_who_indicators to narrow down the 2,200+ indicators until you find exactly what you need for your study.
Pull deep time-series data using get_who_indicator_data. This allows you to track how a metric, like tuberculosis rates, has changed in one country over multiple years. Essential for trend analysis.
The system handles sex disaggregation automatically where the WHO provides it, meaning your comparative analysis is richer than just looking at overall numbers.
Because Vinkius runs everything inside secure isolated sandboxes, you're confident that all sensitive data flow and tool calls are audited via cryptographically signed trails.
See it in action
Tracking NCDs across continents
A researcher needs to know how obesity prevalence has changed in Brazil, India, and Japan since 2010. They run search_who_indicators for 'obesity', then use the code with get_who_indicator_data, comparing all three countries' time-series data automatically.
Assessing rapid post-crisis recovery
A policy analyst needs a quick status check on Haiti after a natural disaster. They run get_who_country_profile to get immediate, high-level metrics like current life expectancy and under-5 mortality rates for an instant assessment.
Building a comparative risk model
A team needs data on both air pollution (a proxy indicator) and child mortality. They use search_who_indicators to find the codes for both, then chain requests to build a dataset that correlates environmental factors with health outcomes.
Benchmarking immunization rates
An international agency needs to compare vaccine coverage (immunization) across several nations. They use search_who_indicators to find the specific code and then run multiple country profiles using get_who_indicator_data to build a standardized comparison.
The honest tradeoffs
Using general web searches
Pasting 'life expectancy Brazil vs Japan' into Google and clicking through multiple WHO pages, then manually downloading CSVs that don't align in format or year.
Instead, run get_who_country_profile for both countries. Then use search_who_indicators to find the specific code, followed by a precise call to get_who_indicator_data to get clean, comparable data sets.
Assuming consistency
Writing a comparison table assuming all countries report data for every single indicator in every year. The resulting manual spreadsheet is filled with gaps and conflicting notes.
Use the structured flow: First, search_who_indicators to confirm availability; then, get_who_indicator_data to let the MCP handle missing years or inconsistent reporting standards.
Trying to get a profile without knowing the codes
Simply asking for 'global health data' and receiving a massive dump of uncurated information that requires hours of filtering.
Start by running get_who_country_profile with an ISO-3 code. This immediately focuses your query on actionable, curated metrics, giving you the essential overview first.
When It Fits, When It Doesn't
Use this MCP if your project requires standardized, time-series health statistics from a major international body like WHO. You need to compare indicators across different countries or track changes over many years (e.g., tracking HIV/AIDS rates). Don't use it if you are analyzing proprietary data—like internal hospital patient records or private insurance claims. For those scenarios, your agent needs a direct connection to the specific database via a purpose-built tool. If your goal is simply general demographic information that doesn't require WHO standardization (e.g., local crime rates), this MCP won't help. This connector works best when you need to standardize and compare metrics across diverse geopolitical regions.
Questions you might have
How do I start with WHO GHO using the search_who_indicators tool? +
You simply tell your agent the topic, like 'tuberculosis incidence.' The search_who_indicators tool finds the correct official code (e.g., MDG_XXXX) that you need to use for all subsequent data calls.
Can I compare multiple countries using get_who_indicator_data? +
Yes, you can structure your query to pull the same indicator code across several country codes (ISO-3) in one go. This is how you build large comparison datasets efficiently.
What information does get_who_country_profile provide? +
This tool gives a broad health snapshot, including life expectancy at birth, healthy life expectancy, and current obesity rates for a single country. It’s perfect for initial research scoping.
Is the data from WHO GHO real-time? +
The data reflects official reports published by the World Health Organization. While it's highly accurate and authoritative, remember that its update frequency depends on global reporting cycles, not instant streaming.
Does using get_who_country_profile require any specific API keys or credentials? +
No, you don't need an API key. This MCP handles authentication securely through Vinkius, meaning your AI client connects directly without storing sensitive keys on disk.
What is the best way to interpret the time-series data returned by get_who_indicator_data? +
The results are structured year by year, providing trend visibility. Since the data includes sex disaggregation where available, you can track changes for different demographics over time.
What format must the country code be when calling get_who_country_profile? +
You must use valid ISO-3 codes (e.g., USA, BRA). These three-letter abbreviations ensure the system pulls accurate and standardized health snapshots for the specified nation.
Can I filter the data returned by get_who_indicator_data beyond just country and year? +
Yes. In addition to filtering by country and year, the tool also supports sex disaggregation. This lets you compare male versus female health outcomes for a specific indicator.
What health topics and indicators does the WHO GHO cover? +
The GHO covers 2,200+ indicators across 40+ themes: life expectancy, child and maternal mortality, infectious diseases (HIV, TB, Malaria, COVID-19), non-communicable diseases (cancer, diabetes, cardiovascular), mental health, immunization coverage, nutrition, water/sanitation, air quality, road safety, tobacco use, and the full Sustainable Development Goals (SDG) monitoring framework.
Is the WHO GHO API free and does it require authentication? +
Yes, the GHO OData API is completely free and open without any authentication. The data is published by the World Health Organization as a public good. There are no API keys, no registration, and no rate limits for reasonable usage. The data is updated regularly with the latest country reports.
Which countries are covered and what country codes should I use? +
All 194 WHO member states plus territories are covered. Use ISO-3 country codes: BRA (Brazil), USA (United States), GBR (United Kingdom), JPN (Japan), DEU (Germany), IND (India), CHN (China), FRA (France), AUS (Australia), ZAF (South Africa), etc. Data availability varies by indicator and country reporting.
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