World Bank Education & Health MCP. Model global health data with 5 indicators.

You're getting immediate access to global metrics data from the World Bank. This server lets your agent pull standardized statistics on human development—the kind of deep, public health numbers that matter. You don't need API keys or any complex setup; it just feeds critical indicators directly into what you're doing.

Tracking Life and Death Rates
You can determine the estimated life expectancy at birth for any country or region using get_life_expectancy. To track mortality trends, run get_infant_mortality to get the rate of infant death per 1,000 live births in a specific area. You'll find it useful comparing these two metrics side-by-side.

Assessing Human Capital and Development
To model overall societal growth, you need more than just averages. Use get_literacy_rate to pull the adult literacy percentage for any population group in a specific year. You can combine this with general development indicators by running get_edu_health_indicator, which pulls almost any World Bank metric related to education or health using its unique code.

These tools let you build comprehensive models of human capital growth.

Analyzing Economic Investment and Spending
When it comes to resources, the numbers tell a story about priorities. You can use get_health_expenditure to see exactly what a country spends on healthcare as a percentage of its total GDP. For education spending relative to economic output, you just run get_edu_health_indicator with the appropriate code for educational expenditure.

This lets you assess resource allocation across entire economies.

Putting It All Together: Advanced Analysis
Your agent can compare development metrics by running queries that pit one indicator against another—for example, comparing life expectancy between two nations or tracking how literacy rates changed over a decade in the same region. You're not limited to just health data; you can combine literacy rates with life expectancy and specific resource spending figures to build deep comparative reports.

For instance, if you want to know how economic priorities affect people, you can pull the percentage of GDP spent on health using get_health_expenditure, then check the resulting life expectancy using get_life_expectancy, all in one workflow. You're not just retrieving single data points; you're building analytical chains that allow your agent to function as a global public health analyst without needing complex database connections.

If you need a specific, obscure indicator—say, the rate of enrollment for vocational training—you don't have to guess how to find it. You use get_edu_health_indicator with the correct code and pull that data immediately. The system handles the complexity so you get clean numbers ready for whatever analysis you need.

You can track historical patterns on infant mortality over time using get_infant_mortality. Similarly, if your research requires a specific year's literacy snapshot, get_literacy_rate delivers it precisely. These tools provide the raw material—the verifiable data points—to answer tough questions about development and human welfare across any region you specify.