# Eurostat Discovery MCP

> Eurostat Discovery — Dataset Catalog Explorer connects you to 7,000+ official European Union statistical datasets. Instead of digging through complex web portals, your AI client lets you search by keyword, check specific data dimensions and code lists, and query any dataset using flexible country or time filters. It's the single point of access for EU economic statistics.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Tags:** eu-statistics, dataset-discovery, metadata-query, economic-data, open-data, statistical-analysis

## Description

Need to figure out what kind of European Union data exists? This connector acts as a master key to Eurostat’s entire catalog. You don't have to guess which dataset you need; your agent first searches across thousands of topics using simple keywords, quickly narrowing the list down to potential candidates. Once you find a promising dataset, it lets you inspect the metadata, showing exactly what dimensions and code lists are available—like checking if 'quarterly' or '2024' is a valid filter. After understanding the structure, you can finally run a targeted query, applying specific country names and time ranges to pull precise data points. Connecting this MCP through Vinkius means your agent handles all that complex navigation, giving you clean, usable statistics without ever needing to visit the source website.

## Tools

### search_datasets
Finds matching dataset codes and names by searching keywords across the Eurostat catalog.

### get_dataset_metadata
Retrieves a full list of available dimensions, code lists, and potential filter values for any given dataset code.

### get_dataset
Queries the specified Eurostat dataset using its code and applying flexible filters like country or time period.

## Prompt Examples

**Prompt:** 
```
Find datasets related to housing prices
```

**Response:** 
```
🔍 **Search: 'housing prices'**

Found 12 matching datasets:
1. prc_hpi_q — House Price Index (quarterly)
2. prc_hpi_a — House Price Index (annual)
3. ilc_lvho02 — Housing cost overburden rate
4. enpr_lphp — Labour cost in housing construction
...

Use get_dataset with code 'prc_hpi_q' to retrieve data.
```

**Prompt:** 
```
What dimensions are available in the GDP dataset?
```

**Response:** 
```
📋 **Metadata: namq_10_gdp**

Dimensions:
- geo: 42 countries (EU27 + EEA + candidates)
- time: 1995-Q1 to 2024-Q3
- unit: 8 options (CP_MEUR, CLV10_MEUR, PC_GDP...)
- na_item: 15 components (B1GQ=GDP, P3, P5G...)
- s_adj: SCA, NSA, CA
- freq: Q (quarterly)

Use these dimension codes as filter parameters.
```

**Prompt:** 
```
Get the house price index for Germany and France since 2020
```

**Response:** 
```
🏠 **House Price Index (2015=100)**

| Quarter | 🇩🇪 Germany | 🇫🇷 France |
|---------|------------|----------|
| 2020-Q1 | 128.3 | 112.1 |
| 2022-Q4 | 152.1 | 129.4 |
| 2024-Q2 | 138.7 | 122.8 |

German prices peaked in 2022 then corrected -8.8%. France more stable.
```

## Capabilities

### Search for relevant datasets
Your agent searches Eurostat's catalog by keyword, returning specific dataset codes and names for your next steps.

### Inspect data structure and filters
The system shows all available dimensions, code lists, and possible filter values before you attempt a query.

### Retrieve targeted statistical data
You execute queries against specific datasets using flexible country and time parameters to pull the actual numbers.

## Use Cases

### Comparing housing market trends
A researcher needs to compare the House Price Index for Germany versus France starting in 2020. They first use `search_datasets` to find the correct code, then call `get_dataset_metadata` to confirm time filters, and finally run `get_dataset` with both countries and years to get a comparative table.

### Identifying available economic variables
A data scientist isn't sure if the dataset contains labor cost information. They use `search_datasets` for 'labour cost', then run `get_dataset_metadata` to see all component codes, confirming the exact field name they need.

### Building a country comparison dashboard
A market analyst needs GDP data for five different countries spanning 25 years. The agent first validates the structure using `get_dataset_metadata`, then executes multiple calls to `get_dataset` in sequence, pulling all required data points into one structured output.

### Validating a niche statistical claim
A user finds an article mentioning a specific agricultural metric. They use the search function to find the corresponding dataset code, then run `get_dataset` with highly precise filters (e.g., '2018' and 'Portugal') to validate the original data point.

## Benefits

- Avoids manual dataset hunting. Instead of clicking through endless web forms, the system first runs `search_datasets` to pinpoint exactly which data code you need.
- Guarantees valid filtering. Before querying, use `get_dataset_metadata` to confirm available dimensions and correct codes, preventing failed API calls.
- Handles complexity. It manages all the intricate logic of country-specific filters (e.g., EU27 vs EEA) so you just ask for 'Germany' or 'France'.
- Streamlines comparison. You can pull related metrics across multiple countries and time periods in one sequence, making comparative analysis quick.
- Saves research time. It gives immediate access to structured data that would otherwise require hours of manual web scraping and data cleaning.

## How It Works

The bottom line is: your agent finds, validates, and extracts complex EU data in a single conversational workflow.

1. First, use the search function to find potential dataset codes by entering a general topic keyword.
2. Next, review the metadata for that code; this step tells you exactly what filters (like country or time) are permitted and available.
3. Finally, run the query using the confirmed dataset code, applying all necessary dimension filters to pull the specific statistics.

## Frequently Asked Questions

**How do I find out what kind of data is in a Eurostat dataset using Eurostat Discovery — Dataset Catalog Explorer?**
You first use `search_datasets` with a general keyword. Once you have a code, run `get_dataset_metadata` to see all the available dimensions and filter options.

**Can I query data for multiple countries at once with Eurostat Discovery — Dataset Catalog Explorer?**
Yes. After validating the structure with metadata, you provide a list of country codes or names directly to `get_dataset`'s filters.

**What if I need data from a topic that is not in Eurostat’s catalog?**
This MCP only connects to the official Eurostat datasets. If your required information isn't part of their published statistics, you won't find it here.

**Which tool should I use first when starting a new analysis?**
Always start with `search_datasets`. This is the most efficient way to narrow down the 7,000+ potential datasets using simple keywords.