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compare_county_unemployment

Compare BLS Local Area Unemployment (LAUS) metrics for up to 10 U.S. counties at a time, using county FIPS codes. Optionally specify year and month; omit for latest data or set month to 13 for annual averages.

Instructions

Compare LAUS unemployment metrics across up to 10 US counties for a single period (never seasonally adjusted). Params: fips is a comma-separated list of 5-digit county FIPS codes (e.g. '06085,48201,17031' for Santa Clara CA, Harris TX, Cook IL). Maximum 10 counties per call. year and month are both optional, but if one is provided the other must be too (omit both to get the latest period the API can resolve). Use month=13 for the annual-average row. Source: US Bureau of Labor Statistics, Local Area Unemployment Statistics (public domain).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fipsYes
yearNo
monthNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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 critical behavior: 'never seasonally adjusted', 'single period', the year/month coupling rule, the 'month=13' annual-average row, and the data source. This goes well beyond the schema and provides the agent with essential constraints.

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 three sentences, front-loaded with the core purpose, followed by precise parameter details. Every sentence adds value, with no repetition or fluff.

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 complexity (3 params, one required), the description fully covers all inputs and usage constraints. An output schema exists, so return-value details are not required. The description is complete enough for an agent to select and invoke the tool correctly.

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 has zero description coverage, but the description compensates fully: it explains fips format with a concrete example, the 10-county limit, the optionality and coupling of year/month, and the special month=13 value. This is exemplar parameter documentation.

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 'Compare LAUS unemployment metrics across up to 10 US counties for a single period', which clearly states the verb (compare), resource (LAUS unemployment metrics), and scope (counties, up to 10, single period). It also distinguishes from sibling tools like compare_state_unemployment by specifying county-level comparison.

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 provides clear usage context: it specifies 'Never seasonally adjusted', 'Maximum 10 counties per call', and the coupling requirement for year/month. However, it does not explicitly name alternatives or when-not-to-use, stopping short of a 5.

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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