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eurostat-mcp-server

by io.github.cyanheads

Get Eurostat Dimension Values

eurostat_get_dimension_values
Read-onlyIdempotent

List all valid values for a specific dimension in a Eurostat dataset (e.g., all unit codes for nama_10_gdp, all geo codes for a regional dataset). Use this when eurostat_get_dataset_info returns more values than the 10-item sample, or to confirm exact codes before querying. For the "geo" dimension, use geo_level to filter by NUTS hierarchy (country, nuts1, nuts2, nuts3). Invalid dimension_value codes silently return no data from eurostat_query_dataset, and are rejected by Eurostat as a fault on eurostat_download_dataset; use this tool to verify codes first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dimensionYesDimension code to retrieve values for (e.g., "unit", "na_item", "geo"). Use eurostat_get_dataset_info to see available dimensions.
geo_levelNoNUTS hierarchy level filter — applies only when dimension is "geo"; passing it with any other dimension is rejected. Options: "aggregate" (EU/EA codes), "country" (2-letter codes, default), "nuts1" (3-char), "nuts2" (4-char), "nuts3" (5-char).
dataset_codeYesDataset code (e.g., "nama_10_gdp").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
valuesNoAll dataset-available values for this dimension. For geo, this is the subset at geoLevel.
geoLevelNoEffective NUTS hierarchy level for the geo value set. Present only for the geo dimension; country is reported when geo_level was omitted.
totalCountNoTotal number of distinct values returned, after geoLevel filtering for geo.
dimensionCodeNoThe dimension code that was queried.
dimensionLabelNoHuman-readable dimension name.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description reveals important behaviors: geo_level filtering only works for the 'geo' dimension, and invalid dimension_value codes behave differently across sibling tools (silent empty results vs. faults). This extra context helps the agent anticipate failure modes and choose the tool correctly.

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

Conciseness5/5

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

The description is front-loaded with the core purpose, then layers usage guidance, special geo dimension behavior, and failure-mode warnings without redundancy. Each sentence adds distinct value, and the length is appropriate for the tool's complexity.

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?

Given the rich input schema, annotations, and output schema, the description covers all essential aspects: what the tool returns, when to use it over siblings, how geo_level works, and why validation matters. There are no obvious gaps that would prevent an agent from selecting or invoking this tool correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3, but the description adds useful semantic context by giving real-world examples like 'nama_10_gdp', clarifying NUTS hierarchy levels for geo_level, and explaining why verifying codes matters. It enriches the parameters beyond the schema's short descriptions.

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 starts with a clear verb-object statement: 'List all valid values for a specific dimension in a Eurostat dataset' and gives concrete examples ('all unit codes for nama_10_gdp'). It clearly distinguishes this from sibling tools by focusing on dimension-value enumeration rather than data querying, browsing themes, or downloading.

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

Usage Guidelines5/5

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

The description explicitly says when to use this tool: when eurostat_get_dataset_info returns more values than the 10-item sample, or to confirm exact codes before querying. It also warns about the consequences of invalid codes in eurostat_query_dataset and eurostat_download_dataset, which makes the usage context concrete and actionable.

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

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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct role: discovery, metadata lookup, dimension value lookup, inline query, bulk download, dataframe staging, and dataframe SQL. The two data-fetching tools are explicitly differentiated as slice vs. whole-dataset routes, and the dataframe tools are clearly separate from direct Eurostat queries.

Naming Consistency4/5

Six of eight tools follow a consistent eurostat_verb_noun pattern, e.g., search_datasets, get_dataset_info, query_dataset. The two dataframe tools invert this to eurostat_dataframe_describe and eurostat_dataframe_query, which is a minor deviation but still readable and predictable.

Tool Count5/5

Eight tools is a well-scoped count for the Eurostat data-access domain. Each tool covers a distinct stage in the workflow from discovery, metadata inspection, targeted querying, bulk download, and post-query analysis.

Completeness5/5

The toolset covers the full expected lifecycle: browse and search to find datasets, get metadata and dimension values to construct queries, query or download data, and stage/query results for further analysis. No significant dead ends or missing operations are evident for the stated purpose.

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