Eurostat

Get Dataset

get_dataset

Read-onlyIdempotent

Fetch statistical data from a Eurostat dataset by dataset code. Optionally filter by country (geo) and time period. Example: get_dataset({ dataset_code: "nama_10_gdp", geo: "DE", time: "2023" }). Common dataset codes: "nama_10_gdp" (GDP), "prc_hicp_manr" (inflation), "une_rt_m" (unemployment). Codes are lowercase, underscore-separated and must come from search_datasets or list_datasets — a guessed or descriptive code is not a Eurostat code.

Input Schema

TableJSON Schema

Name	Required	Description
`geo`	No	Country/region code filter, e.g. "DE" (Germany), "FR" (France), "EU27_2020" (EU aggregate)
`time`	No	Time period filter, e.g. "2023", "2023-01" (Jan 2023), "2023-Q1" (Q1 2023)
`dataset_code`	Yes	Eurostat dataset code from search_datasets or list_datasets, e.g. "nama_10_gdp", "prc_hicp_manr", "une_rt_m". Lowercase with underscores — not a title or a category word.

Output Schema

TableJSON Schema

Name	Required	Description
`label`	Yes	Human-readable label for the dataset
`values`	Yes	Data values indexed by dimension combination
`dataset`	Yes	The dataset code requested
`updated`	Yes	Last update timestamp of the dataset
`dimensions`	Yes	Dataset dimensions with labels and category mappings
`value_count`	Yes	Number of data values in the dataset

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedAug 21, 2026

changedInput schema / properties / dataset_code / description

Previous value: -"Eurostat dataset code, e.g. \"nama_10_gdp\", \"prc_hicp_manr\", \"une_rt_m\""New value: +"Eurostat dataset code from search_datasets or list_datasets, e.g. \"nama_10_gdp\", \"prc_hicp_manr\", \"une_rt_m\". Lowercase with underscores — not a title or a category word."

Changed2 schema fields changedAug 18, 2026

changedInput schema / examples

Previous value: -[
-  {
-    "dataset_code": "nama_10_gdp",
-    "geo": "DE",
-    "time": "2023"
-  },
-  {
-    "dataset_code": "prc_hicp_manr",
-    "geo": "EU27_2020",
-    "time": "2023M06"
-  }
-]New value: +[
+  {
+    "dataset_code": "nama_10_gdp",
+    "geo": "DE",
+    "time": "2023"
+  },
+  {
+    "dataset_code": "prc_hicp_manr",
+    "geo": "EU27_2020",
+    "time": "2023-06"
+  }
+]

changedInput schema / properties / time / description

Previous value: -"Time period filter, e.g. \"2023\", \"2023M01\" (Jan 2023), \"2023Q1\" (Q1 2023)"New value: +"Time period filter, e.g. \"2023\", \"2023-01\" (Jan 2023), \"2023-Q1\" (Q1 2023)"

First observedJun 26, 2026

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only behavior is covered. The description adds operational context like optional filtering and the strict dataset-code requirement, but it does not discuss response shape or edge cases; the bar is lower because annotations carry the safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise and front-loads the core purpose and filters. The example and common-code list are useful, though somewhat redundant with the schema examples, so not every sentence is strictly necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a full input schema, an output schema, and safety annotations, the description provides the remaining essential context: valid dataset-code sourcing, example usage, and filter semantics. No critical information needed to invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents dataset_code, geo, and time. The description adds an example call and common codes, but these largely duplicate the schema examples and property descriptions, providing only marginal additional semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Fetch statistical data from a Eurostat dataset by dataset code') and immediately clarifies the optional filters. It also distinguishes itself from sibling discovery tools by asserting dataset codes must come from search_datasets or list_datasets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: use a valid Eurostat code, optionally filter by geo and time, and do not guess codes. It names the alternative tools that supply valid codes, but it does not explicitly spell out the full decision rule for when to call this tool versus search_datasets or list_datasets.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Eurostat tools Inspect

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