ONS UK Labour Market Statistics

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

Annotations already cover read-only, idempotent, and non-destructive safety. The description adds meaningful behavioral context beyond annotations: data is three-month rolling averages, seasonally adjusted, with a default scope, plus source attribution and license. This is substantial added context and does not contradict any annotation.

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

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

Four efficient sentences: purpose is front-loaded, followed by data format, default behavior, filter guidance, and source/license. Every sentence adds necessary information with no redundancy or filler.

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

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

Despite an output schema covering return values, the description supplies essential calling context: source dataset, rolling-average cadence, seasonal adjustment, default filters, and available metrics. An agent has everything it needs to call the tool correctly and interpret the results at a high level.

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

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

Schema description coverage is 100% and every parameter already has descriptions, enums, defaults, and examples (e.g., age_group: '16-24' (youth unemployment)). The description only adds a high-level filter hint ('Use activity, age_group, sex parameters to filter'), which adds minimal value beyond the schema's already-complete documentation, so baseline 3 is appropriate.

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

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

Description states a specific verb and resource: 'Retrieve UK labour market statistics from the ONS Labour Force Survey (LFS).' It enumerates exact metrics (unemployment/employment/inactivity rates) and breakdowns (age group, sex), clearly distinguishing it from sibling ONS tools like gdp or cpih. However, it does not explicitly name or differentiate from other national unemployment tools, so it misses the full sibling-differentiation bar.

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

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

Provides clear usage context: defaults to overall unemployment rate (16+, all adults, seasonally adjusted) and tells the agent to use activity, age_group, sex parameters to filter. It gives no explicit 'when not to use' or alternative tool routing, but the context is clear enough for an agent to select it appropriately for UK labour market queries.

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