BLS Employment & Wages

get_county_employment

Get county-level unemployment rate, employment, and labor force data.

Returns three time series for a specific county: unemployment rate (percent),
employment count, and labor force size.

Args:
    state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
    county_fips: Three-digit county FIPS code as a string (e.g. '033' for King County, WA).
    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
`county_fips`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the operation ('Get'), the returned data shape (three time series), and default year ranges, but it does not mention data sources, error behavior, or validation requirements for the state/county_fips inputs. This is adequate but not rich.

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 lead sentence states the purpose, a second sentence describes the output, and an Args block lists parameter details. Every sentence is necessary and no redundant or filler content exists.

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 inputs, defaults, and output summary, and an output schema exists to document the return structure. It could explicitly mention when to choose this over sibling tools or describe error edge cases, but for a straightforward retrieval tool it is sufficiently complete.

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?

The schema provides zero descriptions for parameters, so the description fully compensates by explaining every parameter: state with example abbreviations, county_fips with a format example, and start/end year defaults. This is exactly what the agent needs to populate the arguments correctly.

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 opens with 'Get county-level unemployment rate, employment, and labor force data'—a specific verb and resource. It then enumerates the three returned time series, clearly distinguishing this tool from siblings like get_unemployment_rate or get_labor_force_data.

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 'for a specific county' phrasing implies when this tool should be used, but the description never explicitly contrasts it with alternatives such as get_unemployment_rate or get_labor_force_data, nor does it state when not to use it. Usage guidance is only implicit.

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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