BLS Employment & Wages

get_unemployment_rate

Get unemployment rate time series from BLS LAUS data.

Returns monthly unemployment rates for a state or county. Data is returned
in chronological order with year, period, and percentage value.

Args:
    state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
    county_fips: Optional 3-digit county FIPS code (e.g. '033' for King County).
                 If provided, returns county-level data; otherwise state-level.
    start_year: Start year for data (default 2020, min 4-digit year).
    end_year: End year for data (default 2025).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	Yes
`end_year`	No
`start_year`	No
`county_fips`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It covers the data source (BLS LAUS), temporal resolution (monthly), ordering (chronological), return fields (year, period, percentage), and the optional county behavior. It stops short of discussing edge cases or error handling, but for a read-only data retrieval tool, this is adequate and adds substantial context beyond the schema.

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 compact and well-structured: a one-line summary, a few lines of return details, and a clean Args list. Every sentence earns its place, with no redundancy or fluff. The format makes it easy for an agent to parse quickly.

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 tool's moderate complexity, an output schema exists, and the description already covers return values, source, and parameter semantics, this description is fully sufficient. It enables an agent to select and invoke the tool correctly without needing additional context.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully explain parameters. It does so comprehensively: state with examples ('WA', 'CA'), county_fips with a concrete example ('033' for King County) and behavior, start_year/end_year with defaults and a 'min 4-digit year' constraint. This adds meaning that entirely compensates for the bare schema.

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 begins with a clear, specific verb and resource: 'Get unemployment rate time series from BLS LAUS data.' It explicitly distinguishes itself from sibling tools like get_county_employment and get_cpi_data by focusing on unemployment rates. The inclusion of state/county-level detail further clarifies the tool's scope.

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?

The description clearly states what data the tool returns (monthly unemployment rates) and explains the county_fips behavior (county vs. state-level). Though it does not explicitly name alternative tools for comparison, the context is clear enough that an agent can infer when to use this tool instead of siblings, meeting the 'clear context, no exclusions' criterion.

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

All BLS Employment & Wages tools Inspect

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TDQS

A3.7/5.0

Disambiguation2/5

There is significant overlap between tools, particularly get_county_employment, get_labor_force_data, and get_unemployment_rate, which all provide similar employment/unemployment metrics at different geographic levels. The descriptions help somewhat, but an agent could easily misselect between these tools when seeking employment data.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with 'get_' prefix and descriptive suffixes (e.g., get_county_employment, get_cpi_data). The naming is predictable and follows the same convention throughout the set.

Tool Count3/5

With only 4 tools, the set feels thin for a BLS employment and wages server. While the tools cover key metrics, there are likely missing wage-related tools (e.g., get_wage_data, get_occupation_data) that would be expected from such a domain.

Completeness2/5

There are significant gaps in coverage for a BLS employment and wages server. The tools focus heavily on employment/unemployment metrics but completely omit wage data, industry breakdowns, occupation statistics, and other core BLS datasets that would be essential for comprehensive economic analysis.

Resources

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