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Orolol

FactCheck MCP Toolkit

by Orolol

oecd_get_data

Retrieve OECD statistics for specified countries and convert them into time series, enabling comparison and fact-checking of economic claims.

Instructions

Retrieve OECD data and convert to TimeSeries per country.

Args: dataflow: OECD dataflow identifier (e.g. 'QNA'). country: List of country codes (e.g. ['FRA', 'DEU']). time_period: Optional time period filter. filters: Optional additional SDMX filters.

Returns: List of TimeSeries, one per country.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryYes
filtersNo
dataflowYes
time_periodNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It does reveal the return shape (List of TimeSeries, one per country) and implies a read-only retrieval, but it does not mention error behavior, API quirks, limitations, or how filters/time_period affect results.

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 concise and well-organized with a front-loaded one-line summary followed by a compact Args/Returns structure. Itavoids filler and presents the most important information first.

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

Completeness4/5

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

For a relatively simple data retrieval tool with four parameters and an output schema, the description gives enough parameter semantics and return behavior to make an initial call. It could be more complete by naming the sibling tools that are not appropriate and by specifying time_period/filter syntax, but it is not critically incomplete.

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%, so the description must compensate. It explains all four parameters with examples for dataflow and country, and gives meaningful descriptions for time_period and filters. Some details are still vague, such as the exact format for time_period, but overall it adds substantial value beyond the bare schema.

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 clearly states the action, resource, and output: 'Retrieve OECD data and convert to TimeSeries per country.' This is distinct from search-style siblings like oecd_search_datasets, though it does not explicitly name an alternative or contrast with them.

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

Usage Guidelines3/5

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

The intended use is implied: call this when you need OECD data converted into TimeSeries by country. However, it does not state when not to use it or name alternatives such as oecd_search_datasets, eurostat_get_data, or worldbank_get_data, leaving some context to inference.

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