World Bank Data MCP for AI. Compare global health, education, and social metrics instantly.
Works with every AI agent you already use
…and any MCP-compatible client








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World Bank Education & Health gives your agent immediate access to global statistics on life expectancy, infant mortality rates, literacy levels, and government spending.
You can pull precise numbers on health outcomes, education standards, and social welfare indicators across nearly every country without needing any API keys or logins.
What your AI can do
Get life expectancy
Calculates and returns the expected lifespan at birth for a specified population.
Get infant mortality
Provides the rate of infant death for a given time period.
Get health expenditure
Gets the current percentage of a country's GDP dedicated to health care spending.
Retrieve precise life expectancy figures at birth for any country.
Get current data on infant mortality rates to track child health progress.
Evaluate the global adult literacy rate for policy comparison.
Determine how much a government allocates to healthcare as a percentage of its GDP.
Gather multiple metrics, like education/health indicators, for comparative analysis between nations.
Ask an AI about this
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World Bank Education & Health: 5 Tools
These tools let your agent pull specific, reliable metrics—from life expectancy to literacy rates—to analyze global human development data.
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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 World Bank Education & Health on VinkiusGet Life Expectancy
Calculates and returns the expected lifespan at birth for a specified population.
Get Infant Mortality
Provides the rate of infant death for a given time period.
Get Health Expenditure
Gets the current percentage of a country's GDP dedicated to health care spending.
Get Literacy Rate
Determines the percentage of adults who can read and write.
Get Edu Health Indicator
Retrieves any World Bank indicator that measures a combination of education and...
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Choose How to Get Started
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with World Bank Education & Health, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by World Bank Open Data. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Gathering global development statistics used to be a nightmare of PDFs.
To build a report comparing three countries' health status, you used to spend hours hopping between the World Bank site for life expectancy, the UN site for literacy rates, and various national ministry sites for spending. You’d download dozens of spreadsheets, copy-paste data into Excel, and then spend half a day just cleaning up formatting errors before writing a single sentence.
Now, you just ask your agent: 'Compare Country A's life expectancy to its infant mortality rate.' The MCP pulls the numbers instantly and gives them back in clean JSON format. You get structured, usable data right out of the gate.
The World Bank Education & Health MCP provides actionable metrics.
You no longer have to calculate the combined index yourself. Instead of manually finding and merging data points across different domains, you ask for a comparison using get_edu_health_indicator, which handles that complex aggregation for you. This saves hours of tedious math.
What's different now is that your agent doesn't just give you numbers; it gives you the foundation to write policy arguments. You can show exactly how insufficient spending (get_health_expenditure) directly impacts basic survival rates like get_infant_mortality.
What your AI can actually do with this
This MCP connects your agent directly to massive amounts of humanitarian data from the World Bank. Instead of hunting through academic databases for disparate reports, you ask for a comparison, and you get structured numbers back immediately. You can calculate how much a nation spends on healthcare relative to its GDP, or compare adult literacy rates between continents.
This lets you build policy briefs that are backed by hard global metrics.
The real power shows up when you chain this MCP with other data sources. For example, linking education indicators with economic growth data allows your agent to build complex models of development—all within one automated workflow. If you're building an automation across multiple platforms using the Vinkius framework, every tool call gets a cryptographically signed audit trail, meaning you always know exactly where the numbers came from and who called them.
019d7620-3eb0-721b-9829-85f1581c055c Here's how it actually works
The bottom line is, you get reliable, current global statistics instantly without needing to manually query multiple government sites.
Tell your agent exactly what you need—for instance, 'What is the infant mortality rate in Country X?'
The MCP executes the request by calling the World Bank data endpoints internally and pulls the correct figures.
Your agent receives structured data (the numbers) that it can then write into a report or use for further calculations.
Who is this actually for?
Public health analysts and policy researchers who spend too much time gathering raw data from disparate sources. This MCP gives them a single source of truth for global development metrics.
Uses the MCP to compare life expectancy trends against historical spending on healthcare, identifying where policy gaps exist.
Runs comparisons using get_edu_health_indicator and get_literacy_rate to assess a country's overall human capital readiness for economic shifts.
Checks current infant mortality rates across target regions to prove the need for specific intervention funding requests.
What Changes When You Connect
Know a country's true development status by comparing its get_life_expectancy against its get_literacy_rate in one query. This gives a holistic picture that isolated data misses.
Stop guessing about funding needs. Use get_health_expenditure to show exactly how much of the national budget is currently allocated to health care, validating your policy recommendations.
Track progress over time. By running multiple calls for get_infant_mortality or get_life_expectancy, you can build clear visual evidence of improvement or decline in public health efforts.
Build multi-layered reports by chaining this MCP with financial tools. You can correlate low literacy rates directly to insufficient spending tracked via get_health_expenditure.
Eliminate data gaps. Instead of finding one report that combines everything, use the combined power of multiple metrics like get_edu_health_indicator for a single, comprehensive view.
See it in action
Assessing post-conflict recovery
An analyst needs to know if a region is ready for aid. They ask the agent to compare get_infant_mortality rates with current government spending (get_health_expenditure). The agent provides a clear metric of both health risk and financial capacity.
Writing a policy brief on human capital
A researcher compares three nations. They use get_literacy_rate to establish educational baseline, then compare that to get_life_expectancy to see the total health benefit of their education investment.
Evaluating a national curriculum change
The agent is tasked with showing the impact of better schooling. It compares pre- and post-intervention data using get_edu_health_indicator, linking it to predicted gains in life expectancy.
Identifying underfunded health sectors
A policymaker wants to know if a country's spending matches its needs. They check the ratio between required care (implied by get_infant_mortality) and actual allocation using get_health_expenditure.
The honest tradeoffs
Searching for a single 'Human Development Index' number
You search Google or an old academic database for 'HDI 2023 World Bank'. You get one PDF that is hard to parse and outdated.
Ask your agent directly. Request the data points you need—like running a query comparing get_life_expectancy, get_literacy_rate, and get_health_expenditure against each other in a single prompt.
Comparing only economic growth to health
A basic model just plots GDP vs. Mortality Rate, missing the specific educational component.
Use the specialized get_edu_health_indicator tool. This combines both education and health data into one metric, giving you a much deeper comparison.
Using old, cached statistics
Relying on reports from three years ago for current funding decisions.
Always check the most recent metrics. Use get_health_expenditure to ensure your analysis reflects today's financial reality, not yesterday's budget.
When It Fits, When It Doesn't
Use this MCP if your job requires comparing fundamental human development indicators across borders or time periods. You need hard numbers on literacy rates (get_literacy_rate) versus actual lifespan potential (get_life_expectancy). Don't use it if you only need raw economic data, like commodity prices; for that, look at a specialized finance MCP. Also, don't rely solely on get_edu_health_indicator; always cross-reference its results with get_health_expenditure to ensure the improvement is backed by actual national investment. It’s about proving cause and effect, not just stating correlation.
Questions you might have
How do I get started? +
Our World Bank Open Data servers require absolutely zero authentication. You do not need to register, get an API key, or setup webhooks. Just instantly connect and your AI agent can begin querying decades of global data.
Can it analyze government spending on healthcare? +
Yes, your AI agent can retrieve health expenditure as a % of GDP to correlate spending with life expectancy outcomes over multiple years.
Can I query literacy improvements over decades? +
Absolutely. Ask your agent to pull education indicators spanning back decades to demonstrate clear upward trends in global literacy.
What is the scale of the data I can access? +
You have direct access to 64 years of historical data covering 196+ sovereign states and global regional aggregates, powered directly by the World Bank's robust open data initiatives.
How do I use `get_edu_health_indicator` if my client requires credentials? +
You don't need any keys or passwords; this MCP runs with zero required authentication. Vinkius handles the secure connection, so your agent can pull data directly without you ever entering sensitive details.
What happens if I use `get_edu_health_indicator` with an invalid code? +
The MCP returns a clear error message specifying the bad code or missing data field. Your agent can catch this structured output and prompt you to check the indicator list, keeping your workflow going.
Can I use `get_life_expectancy` and `get_infant_mortality` together for a comparison? +
Yes, you can chain these tools to compare outcomes directly. Your agent retrieves both data points—for example, comparing life expectancy against the infant mortality rate—allowing for immediate analysis of public health gaps.
Is there a limit to what I can query using `get_edu_health_indicator`? +
The tool accepts any standard World Bank education or health indicator code. This means you aren't restricted; if the data exists and has an official identifier, your agent can pull it.
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