eurostat-mcp-suite
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_datasetsA | Search Eurostat catalogue by keyword with cursor pagination. |
| browse_themesA | Browse top-level Eurostat themes (Economy, Population, Trade, Environment, Industry, etc.). Returns: Categorized themes with key domain topics and dataset codes. |
| list_popular_datasetsA | List pre-curated high-value Eurostat datasets by category. |
| get_dataset_infoA | Fetch metadata, dimension structures, time ranges, and sample values for a Eurostat dataset. |
| get_dimension_valuesA | List all valid codes and labels for a specific dataset dimension, with optional NUTS regional filtering for 'geo'. |
| query_datasetA | Query and decode statistical observations from a Eurostat dataset with dimension filters and NUTS controls. |
| download_dataset_to_sqlA | Download a complete Eurostat dataset and stage it as a SQL table in the in-memory DuckDB canvas. |
| sql_queryA | Execute a read-only SQL SELECT query against staged in-memory DuckDB tables. |
| list_staged_tablesA | List all staged SQL tables currently available in the DuckDB canvas. |
| describe_tableB | Inspect the schema and first 5 sample rows of a staged SQL table. |
| export_to_csvA | Export a Eurostat dataset directly to a local CSV file on disk. |
| export_multiple_datasetsB | Batch export multiple Eurostat datasets to CSV files in a target directory. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| explore_topic | Guided step-by-step workflow to find and analyze EU statistics on a topic. |
| compare_countries | Template for cross-country EU comparative statistical analysis. |
| trade_analysis | Template for EU international trade & Comext statistical analysis. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_themes_resource | Resource listing Eurostat top-level statistical themes. |
| get_popular_resource | Resource listing popular Eurostat datasets. |
TDQS
Scored across 12 tools
Each tool targets a distinct phase of the Eurostat workflow: discovery, metadata inspection, data querying, SQL staging, and export. Even query_dataset and sql_query are clearly separated—one queries the Eurostat API, the other queries staged DuckDB tables.
Most tools follow a verb-first snake_case pattern (get_, search_, browse_, list_, download_, export_), but sql_query breaks the pattern (noun-led) and export_to_csv vs export_multiple_datasets have inconsistent phrasing. Overall still readable and predictable.
With 12 tools, the server is well-scoped for Eurostat data access, covering discovery, metadata, querying, DL staging, SQL analysis, and export without ballooning in size. Each tool serves a clear purpose in the pipeline.
The tool surface covers the full Eurostat data lifecycle: find datasets, inspect dimensions, query data, stage to SQL, run SQL, and export. Minor gaps include no way to drop staged tables or list all datasets within a theme, but these are not critical for typical use.