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Health Insurance Estimates Tool

health_insurance_tool
Read-onlyIdempotent

Retrieve health insurance coverage estimates for US counties by year, age, income, and race. Get number and percentage of insured and uninsured individuals.

Instructions

Retrieve health insurance coverage estimates from the Small Area Health Insurance Estimates (SAHIE) program. Get single-year estimates of health insurance coverage status for all counties by selected economic and demographic characteristics. Data includes number and percentage of people with and without health insurance, by age group, income level, and race/ethnicity. Available from 2006 to present.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear for health insurance estimates (2006-2023). Default: 2022.2022
variablesNoArray of health insurance variables to retrieve. Common: NIC_PT (number insured), NUI_PT (number uninsured), PCTIC_PT (percent insured), PCTUI_PT (percent uninsured), NIPR_PT (number in poverty range), PCTLIIC_PT (percent low-income insured). Default: [NIC_PT, NUI_PT, PCTIC_PT, PCTUI_PT].
geographyYesGeographic level to query. Options: us (national), state, county.
stateNoState FIPS code (2 digits). Required for county geography, optional for state geography to get a specific state.
countyNoCounty FIPS code (3 digits). Optional to get a specific county within a state.
ageCategoryNoAge category filter. 0: Under 65 years, 1: 18 to 64, 2: 40 to 64, 3: 50 to 64, 4: Under 19 years, 5: 21 to 64 years.
incomeRangeNoIncome range category. 0: All incomes, 1: At or below 138% of poverty threshold, 2: At or below 200%, 3: At or below 250%, 4: At or below 400%.
raceEthnicityNoRace/ethnicity category. 0: All races, 1: White alone not Hispanic, 2: Black alone, 3: Hispanic (any race).
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds behavioral context by stating it retrieves single-year estimates and availability from 2006 to present, which complements the annotations effectively.

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?

Three sentences, front-loaded with the main purpose and key details. Efficient but could be slightly more concise without losing value. No unnecessary repetition.

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?

For a tool with 8 parameters (1 required, 4 enums) and no output schema, the description provides a reasonable overview of data content, dimensions, and year range. It does not explain return format or geography selection details, but the schema handles parameter choices adequately.

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

Parameters3/5

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

All 8 parameters have descriptions in the input schema (100% coverage). The tool description only mentions categories (age, income, race) without adding new semantic details beyond the schema. Baseline score of 3 applies as schema carries the load.

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 health insurance coverage estimates from the SAHIE program, specifying data types (number/percentage insured/uninsured) and dimensions (age, income, race/ethnicity). This clearly differentiates it from sibling tools like poverty_statistics_tool or population_estimates_tool.

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 description implies the tool is for health insurance estimates but does not explicitly state when to use it versus alternatives. No exclusion criteria or comparison with sibling tools are provided, leaving the agent to infer usage context.

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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