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FactCheck MCP Toolkit

by Orolol

eurostat_get_data

Get Eurostat data as per-country time series by providing dataset code and country codes; optionally filter by year and additional parameters.

Instructions

Retrieve Eurostat data and convert to TimeSeries per country.

Args: dataset_code: Eurostat dataset code (e.g. 'nama_10_gdp'). geo: List of country codes (e.g. ['FR', 'DE']). time_period: Optional year filter (e.g. '2022'). filters: Optional additional filters dict.

Returns: List of TimeSeries, one per requested country.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoYes
filtersNo
time_periodNo
dataset_codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does disclose the return shape: a list of TimeSeries, one per requested country, plus optional year and filter arguments. It does not mention API errors, units, time-range behavior, or how filters map to Eurostat query parameters.

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 docstring is organized into Args and Returns with a one-line summary up front; every section is informative. It is not overly verbose, though the parameter list is a conventional format rather than the tightest possible prose.

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

Completeness3/5

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

The description covers all four parameters and the return shape, and an output schema exists so the return contract is further specified. However, it omits selection context among siblings and behavioral caveats, leaving an agent without enough cues for when to call it instead of related tools.

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 0% and the schema only provides types, but the description supplies concrete examples for dataset_code ('nama_10_gdp') and geo (['FR', 'DE']), and clarifies time_period as an optional year filter. The filters parameter remains vague ('additional filters dict'), which prevents a 5.

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

Purpose4/5

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

The description opens with a specific verb and resource: 'Retrieve Eurostat data' and adds a distinctive output shape ('convert to TimeSeries per country'), making the core purpose clear. It does not explicitly contrast with siblings like eurostat_search_datasets or eurostat_get_dataset_structure, so it stops short of a 5.

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

Usage Guidelines2/5

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

No guidance is given on when to choose this tool over nearby siblings such as eurostat_search_datasets, eurostat_get_dataset_structure, or eurostat_get_ranking. The docstring explains how to call it, but not the conditions that should trigger this tool versus alternatives.

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