apibase

OECD Monthly Unemployment Rate

oecd.economy.unemployment

Read-onlyIdempotent

Retrieve monthly unemployment rates from the OECD Labour Force Survey (LFS) for any OECD member country. Returns the unemployment rate as a percentage of the labour force for persons aged 15+, total and by sex, with both seasonally adjusted and unadjusted series. Covers OECD members with typical lags of 1–3 months. The series use standardised ILO definitions of unemployment (persons without work, available, and actively seeking employment). Useful for tracking labour market conditions, comparing countries, and detecting recessions. Source: OECD SDMX API, CC BY 4.0, no auth required, unlimited.

Input Schema

TableJSON Schema

Name	Required	Description
`country`	Yes	ISO 3-letter OECD country code (e.g. USA, GBR, DEU, FRA, JPN, CAN, AUS, KOR, ITA, ESP). See OECD reference area codes for the full list of 38 member countries.
`end_period`	No	End month for unemployment data in YYYY-MM format (e.g. "2024-06"). Defaults to latest available.
`max_series`	No	Maximum number of time series to return (1–100, default 20). Each series is a unique combination of dimensions such as sector, measure, or adjustment type.
`start_period`	No	Start month for unemployment data in YYYY-MM format (e.g. "2023-01"). Defaults to 12 months ago.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No	Present only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
`result`	No	Tool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 22, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful context: source (OECD SDMX API), typical data lag (1-3 months), no auth required, and unlimited usage. This goes beyond annotations without contradicting them.

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 a single, well-organized paragraph that front-loads the main action and includes relevant details (lag, definitions, use cases, source, licensing). It's slightly dense but not redundant or wasteful.

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?

The description covers scope, metric, definitions, lag, source, and licensing, and the output schema exists to detail return format. It's complete enough for an agent to call correctly; missing details like pagination are handled by schema parameters (max_series).

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 parameters are well documented. The description adds a little context about the data series (age 15+, by sex, seasonally adjusted/unadjusted) but not specifically about the parameters themselves. Baseline of 3 is appropriate.

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 (Retrieve) and a clear resource (monthly unemployment rates from the OECD Labour Force Survey for OECD member countries). It also specifies the metric (percentage of labour force, age 15+, by sex, seasonally adjusted/unadjusted) which distinguishes it from sibling tools like oecd.economy.gdp or bls.macro.unemployment.

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?

It provides clear usage context (tracking labour market conditions, comparing countries, detecting recessions) but does not explicitly name alternative tools or state when not to use it. This is a 'clear context, no exclusions' scenario, earning a 4.

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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