Aragón Open Data MCP for AI. Find regional stats from the Government of Aragón.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Aragón Open Data connects your AI agent directly to the Government of Aragón's public record portal. You can query massive regional datasets, check API schemas, and search official government metadata using natural language prompts.
What your AI can do
Count datasets
Counts and reports the total number of available datasets in the catalog.
Get organization
Gets detailed information about a particular data publisher or organization.
Get dataset
Retrieves full details about a specific dataset package.
List every dataset package or data view registered in the public catalog so you know what records exist.
Fetch a sample of actual data records from a chosen view, supporting filters and pagination to narrow down results.
Retrieve column names and data types for any given dataset view, confirming if the data is structured how you expect it.
Find specific datasets, tags, or publishing organizations using Solr-powered keyword searches.
Run formal SPARQL queries against established knowledge graphs like Aragopedia for highly structured data relationships.
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Aragón Open Data: 15 Tools for Data Exploration
These tools allow you to catalog everything available in the public data portal, from listing datasets and viewing schemas to running advanced SPARQL queries.
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 Aragón Open Data on VinkiusCount Datasets
Counts and reports the total number of available datasets in the catalog.
Get Organization
Gets detailed information about a particular data publisher or organization.
Get Dataset
Retrieves full details about a specific dataset package.
Get Tag
Retrieves details for a specified metadata tag.
List Groups
Lists every defined theme or group used to categorize datasets.
List Organizations
Retrieves a list of all organizations that publish data in the region.
List Datasets
Lists all available dataset packages in the public catalog.
List Tags
Lists every defined metadata tag available for filtering datasets.
List Views
Retrieves a list of all distinct data views currently available in the catalog.
Most Downloaded Datasets
Identifies and lists the datasets that have been downloaded most frequently.
Newest Datasets
Fetches a list of recently added or updated datasets.
Preview Data
Samples and shows the first few records from a selected data view or resource.
Query Sparql
Executes advanced SPARQL queries against specific regional ontologies.
Search Datasets
Finds datasets by running a targeted search query across the entire catalog.
Show Columns
Displays column names and data types for a given dataset view.
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Aragón Open 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
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- 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 Aragón 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 15 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Finding specific public records feels like a scavenger hunt.
Today, finding regional data means navigating multiple government portals. You start by searching one site for housing statistics, download the CSV; then you have to log into another portal for air quality metrics, and repeat the whole process. This requires constant context switching and tedious copy-pasting between different systems just to get a full picture.
With this MCP, you don't navigate portals. You ask your agent one question—for instance, 'Show me records linking housing data to local demographics.' Your agent handles the search across all available datasets, pulling together the required views and presenting the results directly.
Using Aragón Open Data for Schema Inspection
Manually checking a dataset's schema means clicking through multiple tabs in an API documentation portal. You have to drill down into metadata pages, hoping the column names and data types are exposed clearly enough that you don't waste time on bad assumptions.
Now, just ask your agent to run 'show_columns'. It instantly returns a clean list of every field name and its type for any view. That’s it. You know exactly what fields you have before writing one line of code.
What your AI can actually do with this
You need deep insights from local government data but don't have time for manual CSV downloads or complex database joins. This MCP lets your agent browse the entire Aragón Open Data catalog—regional statistics, environmental readings, population counts—and pull records directly into your workflow. You tell it what you want to know; it handles the search and filtering across dozens of official views.
It’s like having a dedicated data librarian who knows where every single public record lives. Need to understand if a dataset is current? Run a schema check first. Want to find out which organizations publish specific types of records? The agent finds those publisher details automatically. For the most advanced queries, you can run SPARQL against known ontologies like Aragopedia.
Accessing this level of regional data usually requires specialized API calls, but here, all that complexity is handled by connecting through Vinkius.
019e3866-b311-7122-9d3c-70c3170832fe Here's how it actually works
The bottom line is, you talk to it naturally, and it translates that into precise data calls against government records.
Subscribe to this MCP and enter your API key (if required by the specific endpoint).
Tell your AI client what you need—for example, 'Show me all available views related to housing.'
The agent executes the correct underlying search or query using the appropriate tool and returns structured data.
Who is this actually for?
This MCP targets researchers, analysts, and developers who routinely need high-volume, structured public record access. If your job involves connecting disparate governmental datasets—like linking environmental readings to demographic changes—you need this tool.
Needs to quickly check if the right metrics exist in a view, or pull sample data using 'preview_data' before building a full report.
Requires searching for specific themes ('list_groups') across multiple datasets and running complex graph queries via 'query_sparql'.
Must inspect the API schema using 'show_columns' to ensure their application code handles data types correctly.
What Changes When You Connect
Stop manual data collection. Instead of downloading dozens of CSV files, your agent can execute 'list_views' and then use 'preview_data' to see exactly what records you need, instantly.
Pinpoint specific sources fast. If you know the topic but not the dataset name, run 'search_datasets' or filter by tags using 'list_tags' until you zero in on the right package.
Handle complex data structures without writing SQL. Use 'query_sparql' to ask questions about relationships (e.g., linking demographics to economic zones) that basic views can't show.
Save time debugging APIs. Before coding, run 'show_columns' on a view. You immediately confirm the field names and data types you need for your application logic.
Keep track of what matters. Run 'most_downloaded_datasets' to see which government records are most frequently used by others, helping you prioritize your research.
See it in action
Linking health data to housing trends
A researcher needs to correlate local air quality readings with census demographics. They ask the agent to find relevant views for both topics and then use 'query_sparql' to join them, avoiding weeks of manual data alignment.
Building a dashboard on current legislation
A developer needs to know which organizations publish records related to zoning laws. They ask the agent to run 'list_organizations', then filter by theme using 'list_groups' to get a clear list of data sources.
Auditing public transparency reports
A journalist needs to check if all required annual expenditure reports are available. They use 'search_datasets' and then run 'get_dataset' on the top results to verify publication dates and scope.
Verifying data pipeline inputs
An internal analyst has a new dashboard that relies on public records. They first use 'list_views' and 'show_columns' to validate the underlying schema before writing any integration code.
The honest tradeoffs
Assuming data is in one place
A user just searches for 'housing data' once, gets a list of 5 datasets, and assumes they all contain the same type of information.
Don’t stop at the search results. Use 'get_dataset' on each promising result to get full context, then run 'list_views' to see exactly what specific views are available for data extraction.
Trying to query without knowing schema
A developer tries to build a SPARQL query referencing a column name they assume is standard (e.g., 'population_count'), but it fails because the actual view uses a different field name.
Always run 'show_columns' first on the target view ID. This confirms the precise spelling and data type of every field, preventing query failures.
Confusing search with listing
A user asks to see all datasets about 'tourism,' but then manually checks only one list without verifying if other related tags or groups exist.
Don't rely on a single query. Use 'list_tags' and 'list_groups' separately. This reveals the full breadth of available metadata, ensuring you don't miss adjacent data.
When It Fits, When It Doesn't
Use this MCP if your goal is deep structural knowledge: you need to know what data exists (using 'list_datasets', 'list_views'), how it’s structured ('show_columns'), or how specific records relate across different themes ('query_sparql'). Don't use this if you just want a quick, general overview of the government's mission—for that, reading the main portal is enough. If your goal is simply to find contact info for the publishing agency, use 'get_organization'. If you only need to know what kind of data exists, running 'list_groups' gives you the taxonomy first. Only run 'preview_data' after confirming the right view and schema via these preliminary tools.
Questions you might have
How do I check what data is available using list_views? +
Running 'list_views' provides a comprehensive directory of every specific, prepared dataset view. This tells you the exact ID and name of the record set you need to query or preview.
Can I run complex queries with query_sparql? +
Yes. 'query_sparql' allows you to execute formal graph queries against defined ontologies, enabling you to connect related data points that standard dataset views might separate.
What is the difference between list_datasets and search_datasets? +
Use 'list_datasets' for a comprehensive inventory of all available packages. Use 'search_datasets' when you know keywords (like 'tourism') and want to narrow down which specific datasets match that topic.
Do I need an API key to preview data using preview_data? +
While sometimes an API key is necessary for certain endpoints, the agent handles the authentication flow. If public access is available, it retrieves a sample of records directly through 'preview_data'.
How do I determine the exact structure and column types for a view using show_columns? +
It returns a detailed list of every available field in that specific data view. You'll get the column name, its associated data type (like string or integer), and whether it allows null values.
What information does get_organization provide about data publishers? +
This tool retrieves detailed metadata about the entity publishing the dataset. You can find the organization's full name, a description of its scope, and its role within the region’s open data network.
If I want to know which datasets are most popular, should I use most_downloaded_datasets? +
Yes. This tool aggregates download statistics across all public records, ranking them by usage frequency. It helps you quickly identify the key data sources that other users rely on.
How can list_tags help me narrow down my search for specific topics? +
It lists every predefined tag available in the catalog. Use this list to see all possible themes, allowing you to filter massive amounts of data by subject matter before running a full search.
Can I filter the data preview to see only specific records? +
Yes! Use the preview_data tool and provide a JSON string in the filters parameter (e.g., {"entidad": "ARANDA"}). This allows you to restrict the results to exactly what you need.
How do I find the structure and data types of a specific view? +
You can use the show_columns tool by providing the view_id. It will return a detailed list of all columns, their descriptions, and their technical data types.
Is it possible to search for datasets by keywords or topics? +
Absolutely. Use the search_datasets tool with the q parameter to perform a Solr search across the entire CKAN catalog for relevant datasets.
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