Eurostat Discovery MCP. Query EU statistics across 7,000+ datasets.
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Eurostat Discovery — Dataset Catalog Explorer provides the master key to all European Union statistics. Use it to search, inspect, and query 7,000+ official datasets from Eurostat.
Find datasets by keyword using `search_datasets`, understand required filters with `get_dataset_metadata`, and retrieve specific data using `get_dataset`. It's the central utility for any agent needing reliable, structured EU economic data.
What your AI agents can do
Get dataset
Queries any Eurostat dataset by code, allowing you to apply flexible filters for country and time.
Get dataset metadata
Shows all available dimensions, code lists, and possible filter values for a dataset code.
Search datasets
Searches the Eurostat dataset catalog by keyword to return matching dataset codes and names.
The search_datasets tool returns a list of matching dataset codes and names from the Eurostat catalog.
The get_dataset_metadata tool shows all available dimensions, code lists, and possible filter values for a given dataset code.
The get_dataset tool queries a specific Eurostat dataset by code, applying flexible filters for country and time.
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Supported MCP Clients
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019d7591get dataset
Queries any Eurostat dataset by code, allowing you to apply flexible filters for country and time.
019d7591get dataset metadata
Shows all available dimensions, code lists, and possible filter values for a dataset code.
019d7591search datasets
Searches the Eurostat dataset catalog by keyword to return matching dataset codes and names.
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Make Your AI Do More
Start with Eurostat Discovery — Dataset Catalog Explorer, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
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What you can do with this MCP connector
Your AI client can access the Eurostat Discovery server to find, inspect, and query over 7,000 European Union datasets. It's the central utility for any agent needing reliable, structured EU economic data. search_datasets searches the Eurostat dataset catalog by keyword, returning matching dataset codes and names. get_dataset_metadata shows all available dimensions, code lists, and possible filter values for a given dataset code. get_dataset queries a specific Eurostat dataset by code, letting you apply flexible filters for country and time.
How Eurostat Discovery MCP Works
- 1 First, use
search_datasetsto find the specific dataset code you need. Give it a keyword (e.g., 'housing prices'). - 2 Next, use
get_dataset_metadatawith the code found in step one. This reveals all valid dimensions and filter options. - 3 Finally, use
get_dataset, passing the dataset code and the required filters (country, time, dimension codes) to get the data.
The bottom line is: you search for the data, check the rules for that data, and then retrieve it.
Who Is Eurostat Discovery MCP For?
This is for data analysts and researchers who deal with complex, multi-source governmental data. If your job requires knowing what EU statistics exist and then querying them programmatically, you need this. It handles the complexity of large, structured catalogs so you don't have to manually sift through dozens of websites.
Uses the tools to build reports by finding the correct dataset code via search_datasets, checking the dimension limits via get_dataset_metadata, and running targeted queries with get_dataset.
Relies on the server to locate specific economic indicators (like housing prices or GDP) across different EU countries and time periods, allowing them to compare trends quickly.
Uses the metadata tools to validate schema and available filter values before running a query, ensuring the data retrieved via get_dataset is correctly structured for modeling.
What Changes When You Connect
- Find what you need: Instead of guessing, use
search_datasetsto pinpoint the exact dataset code for any economic indicator. It searches 7,000+ databases by keyword. - Validate filters first: Don't run a query only to fail. Run
get_dataset_metadatafirst. This shows you every dimension, code list, and frequency option available for that specific dataset. - Automate complex queries: Use
get_datasetto pull data for multiple countries and time periods in one call. You don't need to write complex SQL or manage multiple API endpoints. - Stop website hopping: This server centralizes access to EU data. You don't have to jump between different Eurostat pages to find the right data source or understand its structure.
- Maintain consistency: By managing the data lifecycle (Search -> Metadata -> Query) through the tools, your agent ensures that the dataset code and filters used are valid and up-to-date.
- Directly target EU data: The tools are specific to Eurostat. They don't generalize; they give you direct access to official EU statistics, like GDP or housing indices.
Real-World Use Cases
Comparing Housing Prices Across EU Members
A researcher needs to compare house price trends in Germany and France since 2020. They use search_datasets to find the correct house price index code. Then, they run get_dataset_metadata to confirm time and country filters. Finally, they use get_dataset to pull a structured table comparing the required metrics.
Tracking GDP Growth for a Specific Sector
A data scientist wants to see GDP trends for a specific industrial component. They use search_datasets for 'GDP'. The results give them the main dataset code. They check the dimensions with get_dataset_metadata to ensure the component code is valid, then execute get_dataset to pull the final time-series data.
Finding Data on Environmental Impact
An analyst is looking for environmental data but isn't sure what keywords to use. They start by running search_datasets with 'environment' or 'pollution'. This surfaces multiple codes. They then use get_dataset_metadata on the most promising code to see if pollution levels are even tracked.
Building a Comprehensive Economic Dashboard
A platform developer needs multiple data points (e.g., labor costs, inflation, and prices) for a dashboard. They use the search_datasets tool repeatedly to gather all necessary codes, then chain the subsequent get_dataset_metadata and get_dataset calls to build a single, cohesive data stream.
The Tradeoffs
Searching manually on the website
Spending 30 minutes clicking through the Eurostat website, trying to remember which dataset code applies to what metric. You end up getting lost in the navigation and can't easily compare data across different years.
→
Use search_datasets first. This tool instantly surfaces matching dataset codes and names. Then, use get_dataset_metadata to confirm the filter requirements before running get_dataset.
Guessing the dataset code
Assuming the code for 'housing prices' is prc_hpi_q without checking. This leads to an API error or, worse, retrieving data for the wrong metric entirely, wasting time and trust.
→
Always run search_datasets first to confirm the code. Then, use get_dataset_metadata to validate that the code you found supports the specific dimensions (like country or time) you intend to use.
Running the query without filters
Calling get_dataset only with the code, expecting all historical data. This either fails due to required filters or returns an overwhelming, unusable data dump.
→
Check the required filters first. Use get_dataset_metadata to see the valid dimensions (like geo or time) and then pass those required parameters when calling get_dataset.
When It Fits, When It Doesn't
Use this server if your job requires querying structured, official European Union statistics. You need to find, understand, and retrieve data from a massive, complex catalog like Eurostat.
Don't use this if you need data from a single, simple source (e.g., a company's internal sales database). For those, a standard database connector is better. Also, if your goal is just general data visualization and you don't care about the source's official metadata, you'll be fine. But if you need validation (knowing the correct dimension codes and time ranges), this is non-negotiable. This server forces you to follow the correct workflow: search_datasets -> get_dataset_metadata -> get_dataset.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Eurostat. 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 server provides 3 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Figuring out which dataset code you need shouldn't take 20 minutes.
Right now, if you need a metric like 'labour cost in housing construction,' you're stuck. You spend time browsing the Eurostat site, clicking through categories, and cross-referencing dozens of pages just to find the correct dataset code. You end up copying a code, which is often fragile and hard to verify.
With the Eurostat Discovery MCP Server, you just ask your agent to search. It runs `search_datasets` and gives you the list of codes immediately. You skip the navigation and go straight to the data.
Get the dataset you need with the `get_dataset` tool.
The manual process involves remembering which filters are mandatory for a dataset—is it annual? Quarterly? Does it need a specific country code? You have to check the documentation pages for each dataset individually, which is slow and prone to error.
The server handles this. You use `get_dataset_metadata` to confirm the filters, then pass them to `get_dataset`. You get clean, actionable data without worrying about the underlying API complexity.
Common Questions About Eurostat Discovery MCP
How do I find a dataset code using `search_datasets`? +
Run search_datasets and pass a keyword (e.g., 'housing prices'). The tool returns a list of matching codes and names, so you know exactly what you're working with.
What does `get_dataset_metadata` show me? +
It shows all available filters for a dataset. This includes dimensions (like country or unit), code lists, and the possible frequency options. It’s how you validate your query parameters.
Can I use `get_dataset` to get data for multiple countries? +
Yes. You pass the specific dimension codes and country filters to get_dataset. You don't need to run the query multiple times for each country.
Is the data from `get_dataset` real-time? +
The data reflects the latest statistics available in the Eurostat catalog. It’s structured and ready for analysis, but remember it's derived from official reporting cycles.
How do I use `get_dataset_metadata` to figure out the best filters for a specific query? +
The get_dataset_metadata tool provides a complete inventory of available filters. It lists all dimensions, code lists, and valid filter values (like country codes or time periods) so you know exactly what you can pass to get_dataset.
If I run `get_dataset` and get an error, what should I check first? +
First, verify the dataset code. If that's correct, use search_datasets to confirm the code, and then run get_dataset_metadata to ensure your filters are using the correct dimension names and valid codes.
Does `search_datasets` handle complex search criteria like combining multiple keywords? +
It searches across 7,000+ datasets using keywords. You can provide multiple terms in a single prompt, and the tool returns any datasets matching those combined criteria.
What is the typical scope or time range I can expect when querying with `get_dataset`? +
The scope depends entirely on the dataset and its metadata. You must use get_dataset_metadata first to determine the available time and geographical dimensions and their corresponding ranges.
How many datasets does Eurostat have? +
Over 7,000 datasets covering 9 statistical themes. Use the search_datasets tool to find any dataset by keyword, then get_dataset_metadata to understand its dimensions before querying.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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