CDC WONDER MCP for AI. Analyze public health trends from the CDC.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
CDC WONDER (Epidemiologic Data) connects your AI agent directly to the massive public health databases of the CDC WONDER system.
You can query raw data on mortality rates, birth statistics, and vaccine adverse events using natural language prompts. It bypasses complex web forms, giving you structured access to critical epidemiological research data.
What your AI can do
Query wonder database
Runs complex queries against CDC WONDER databases using specific ID and parameter inputs (B_, M_, V_, etc.).
Retrieve detailed records on causes of death (D76) for specific regions and timeframes.
Generate reports on natality data, allowing you to track maternal health metrics by location.
Extract raw adverse event data from the VAERS database for safety monitoring research.
Execute complex, ad-hoc queries using standard CDC parameters to refine data extraction precision.
Ask an AI about this
Waiting for input…
CDC WONDER (Epidemiologic Data): 1 Tool
Use the available tools to execute precise database queries against massive CDC public health datasets.
Make your AI actually useful.
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 CDC WONDER (Epidemiologic Data) on VinkiusQuery Wonder Database
Runs complex queries against CDC WONDER databases using specific ID and parameter inputs (B_, M_, V_, etc.).
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with CDC WONDER (Epidemiologic Data), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CDC WONDER. 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually pulling public health data is a nightmare of clicks and forms.
Think about it: You have to open the WONDER site. Then you select 'Mortality,' then you choose the specific database ID, input year ranges, set location filters for states or counties, and finally, hit submit. Repeat that process for birth records, vaccine data, and every other variable you want to compare.
With this MCP, you just tell your agent what you need—say, 'Compare D76 mortality rates between State A and State B in 2018.' The system handles the sequence of clicks, the correct parameter inputs, and the complex database calls. You get a clean data payload back, instantly.
Getting structured data using `query_wonder_database`
The process you eliminate is the cross-checking of disparate data formats. Before this MCP, if you wanted three separate metrics, you'd get three different files—each requiring a unique clean-up routine just to align the columns and date formats.
Now, all results are centralized into one structured output. You talk to your agent, it runs `query_wonder_database`, and you immediately have aligned data ready for analysis. That’s a massive time saver.
What your AI can actually do with this
This MCP lets you treat huge public health datasets like a simple conversation. Instead of spending hours navigating the CDC WONDER website and filling out confusing forms—the kind that make you question your career choice—you just talk to your agent. It handles the complex query structure for you. You can ask it for mortality rates in specific years, look at birth statistics across different states, or pull raw vaccine safety data from VAERS.
The system pulls structured results directly into your workflow, no CSV downloads required. If you're working with public health policy or academic research, this is huge. When you connect to the Vinkius catalog, you get access to tools like this one and thousands of others, keeping all your specialized services in one place.
019e3875-147a-7027-b9ce-2356b037d1b6 Here's how it actually works
The bottom line is, you talk naturally, and the system handles all the complicated database logic.
Subscribe to this MCP and provide your necessary API access token or accept the required Data Use Agreement.
Send a natural language request through your AI client, specifying the type of data you need (e.g., 'Mortality rates for 2021 in Texas').
The MCP executes the complex query against the CDC WONDER databases and returns the structured epidemiologic data directly to your agent.
Who is this actually for?
This MCP is for researchers and policy makers who spend too much time wrestling with arcane government websites. It's for the public health official who needs real-time data on trends without a dedicated database team, or the academic who just wants to get back to analyzing instead of formatting.
Runs comparative analysis on mortality and birth statistics across multiple states for research papers.
Monitors current health trends or vaccine safety reports by querying the VAERS database directly into a meeting summary.
Fetches structured JSON data feeds for integration into large-scale analytical pipelines, skipping manual ETL steps.
What Changes When You Connect
Stop navigating complex web interfaces. Instead of clicking through dozens of filters across multiple tabs, your agent builds and runs the perfect query in a single step.
Get structured JSON output instead of PDFs or raw CSVs. Data Scientists can pull clean, machine-readable records for immediate integration into analytical tools.
Cover multiple data types (mortality, births, vaccine safety) from one connection point. You don't need three different databases; you just need the CDC WONDER MCP.
Speed up research cycles. Quickly compare mortality rates between distinct timeframes and regions without needing manual data collection or clean-up.
Access raw source material. The tool lets you pull foundational, raw epidemiologic records that are necessary for deep academic analysis.
See it in action
Analyzing Outbreak Patterns
A public health official needs to see how mortality rates changed in three specific metro areas during the last five years. Instead of manually running separate reports for each region and year, they prompt their agent: 'Compare D76 data for these three cities over this period.' The MCP executes multiple queries and returns a consolidated dataset.
Academic Research on Birth Trends
An epidemiologist is writing a paper on maternal health. They use the tool to pull D10 natality data for California, cross-referencing specific location prefixes (F_) and time periods to build a precise statistical model.
Monitoring Vaccine Safety
A researcher needs an immediate snapshot of adverse event reports. They instruct their agent: 'Fetch VAERS data for the last quarter, filtering by vaccine code X.' The MCP handles the specific database query and delivers summarized counts.
Comparative State Studies
A policy analyst needs to compare how different states handled a public health crisis. They ask the agent to run a comparative analysis across several states, using structured queries for consistency, saving hours of cross-platform data compilation.
The honest tradeoffs
The Web Form Crawl
Trying to manually pull comparable birth statistics from the CDC WONDER website's web interface. This means opening 5 different tabs, remembering which filters you applied, and then downloading multiple non-standardized CSV files.
Use query_wonder_database. Simply tell your agent what data you need—the database ID and parameters—and it handles the complex querying process automatically.
The Spreadsheet Guess
Getting frustrated and just guessing at which data fields are needed, leading to incomplete or inconsistent datasets that require hours of manual clean-up in Excel.
Use query_wonder_database with the full scope of parameters (B_, M_, V_, F_, O_) available. This ensures you get precise, structured records from the source.
The Version Skim
Looking at a report and assuming all data is current without verifying the underlying dataset's completeness or scope.
Always specify the exact timeframes and database IDs you need when calling query_wonder_database. This forces precision into your analysis.
When It Fits, When It Doesn't
Use this MCP if your core requirement is accessing structured, raw data directly from the CDC WONDER system. You must be dealing with mortality records (D76), birth statistics (D10), or vaccine adverse events (VAERS). Don't use it if you just need general public health information that could come from a search engine; this is for deep dataset querying.
Don't rely on the MCP if your analysis requires data that isn't maintained by CDC WONDER. For niche, real-time streaming metrics (like current local hospital bed counts), look into specialized APIs instead. However, when you need to query massive historical or academic datasets, this tool is unmatched in its depth.
Questions you might have
How do I use the query_wonder_database tool with multiple parameters? +
You provide all necessary filters (like location, year, and age groups) in a single prompt. The agent structures this into the required JSON object for the underlying database call.
Does CDC WONDER MCP handle data from different sources like D10 and VAERS? +
Yes. You can query multiple distinct datasets—mortality, births, vaccine safety reports—using the same connection point via your agent's prompt.
Is this better than just searching Google for CDC data? +
Absolutely. Google gives you links and summaries; this MCP executes precise database calls to retrieve structured records, giving you usable data, not just articles.
What prefixes are required when calling query_wonder_database? +
The tool requires specific parameters like B_, M_, V_, F_, or O_ prefixes. Your agent handles the correct JSON structure for these inputs based on your request.
How do I handle authentication when calling query_wonder_database? +
You must provide a valid CDC WONDER API Access Token or ensure the required Data Use Agreement is accepted first. Your AI client will prompt you for these credentials if they aren't already configured in your MCP environment.
What happens if query_wonder_database encounters rate limits? +
If a query fails due to excessive usage, the system recommends implementing an exponential backoff strategy. You should adjust your agent workflow to pause and retry the request after increasing time intervals.
What format does query_wonder_database return its results in? +
The tool returns structured JSON data, making it ready for immediate ingestion into analytical pipelines. This clean output allows your agent to process and interpret the epidemiologic records without manual parsing.
Can I use query_wonder_database for real-time health event monitoring? +
No, this MCP accesses historical and published CDC WONDER datasets. It is designed for deep academic research and trend analysis, not continuous, live stream reporting of current health events.
Which databases can I access using this server? +
You can query any database supported by the CDC WONDER API by providing its ID to the query_wonder_database tool. Common IDs include D76 (Detailed Mortality), D10 (Natality), and VAERS (Vaccine Adverse Event Reporting System).
How should I format the parameters for a query? +
Parameters should be provided as a JSON object using the standard CDC prefixes: B_ for by-variables, M_ for measures, V_ for values, F_ for filters, and O_ for other options. The query_wonder_database tool handles the XML conversion for you.
Do I need to include the 'accept_datause_restrictions' parameter? +
No. The query_wonder_database tool is designed to handle the data use restrictions agreement internally. You only need to provide the specific database ID and the analytical parameters for your search.
We've already built the connector for CDC WONDER. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 1 tools are live and waiting.
You're up and running in seconds.
Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.