BLS Employment & Wages

get_labor_force_data

Get labor force participation, employment, and unemployment counts for a state.

Returns three time series: labor force size, employment count, and
unemployment count. All values are in thousands of persons.

Args:
    state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
    start_year: Start year for data (default 2020).
    end_year: End year for data (default 2025).

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are present, so the description must carry the burden. It discloses the return format (three time series, values in thousands) but does not mention data source, error handling, or any side effects. It implies a read-only operation but doesn't explicitly state it.

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 concise and well-structured with a clear purpose statement, return summary, and an Args block. Each sentence serves a purpose, though the phrase 'labor force participation' is slightly inconsistent with 'labor force size' in the returns.

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 tool has a simple interface (1 required param) and an output schema exists, but the description lacks usage context such as when to use this versus siblings, data source, and handling of invalid inputs. It covers return values, which is helpful, but overall completeness is moderate.

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%, but the description compensates by explaining each parameter: state (two-letter abbreviation with examples), start_year, and end_year (with defaults). It adds practical syntax guidance beyond the raw schema, though it lacks constraints like valid year ranges.

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 clearly states the tool retrieves labor force participation, employment, and unemployment counts for a state, and specifies the three time series returned. This distinguishes it from siblings like get_unemployment_rate (rate vs counts) and get_county_employment (county vs state level).

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 use this tool versus alternatives such as get_unemployment_rate or get_county_employment. The context implies state-level data, but no explicit exclusions or alternative recommendations are provided.

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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