US County Data Skill

Overview

County Data works by analyzing the county name provided and getting data about the US county. It uses advanced algorithms to get data such as average income, area, and more based on the county name and returns the county data.

Live Test US County Data Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as USCountyDataSkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "USCountyDataSkill",
  "arguments": {
    "state": "MO",
    "county": "Jackson"
  }
}

You do not name the tool yourself; the model picks it. Asking about MO in the terms this skill covers is enough for it to reach for USCountyDataSkill on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`state`Required	string	The two letter name of the US state the county is in (e.g., MO) length 2–2
`county`Required	string	The name of the US county to get data about (e.g. Jackson)

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "name": "jackson county",
    "state": "MO",
    "age": {
      "0-4": 0.0644100874666257,
      "5-9": 0.06532756955438819,
      "10-14": 0.06595060390235714,
      "15-19": 0.05967616438434107,
      "20-24": 0.059725950234064615,
      "25-29": 0.08192474939936928,
      "30-34": 0.07523353119652466,
      "35-39": 0.06868029092005673,
      "40-44": 0.05831060964906666,
      "45-49": 0.058805623240603636,
      "50-54": 0.05858087569042305,
      "55-59": 0.0674783182624454,
      "60-64": 0.06208437705811146,
      "65-69": 0.05099777955110233,
      "70-74": 0.03891546504962227,
      "75-79": 0.026437708656052324,
      "80-84": 0.01748905778145719,
      "85+": 0.01997123800338828
    },
    "male": 339932,
    "female": 363079,
    "health": {
      "poorhealth": 20.588989742,
      "physicallyunhealthydays": 4.247736361,
      "mentallyunhealthydays": 4.8111015035,
      "lowbirthweightpercent": 9.1518749808,
      "smokerspercent": 20.957241772,
      "obesitypercent": 31.5,
      "foodenvindex": 7.5,
      "physicallyinactivepercent": 23.2,
      "excessivedrinkingpercent": 18.940103365,
      "alcoholimpaireddrivingdeaths": 152,
      "teenbirthrate": 31.109351559,
      "uninsured": 12.486314662,
      "withannualmammogram": 45,
      "vaccinated": 51,
      "childreninpoverty": 19.6,
      "80thpercentileincome": 108296,
      "20thpercentileincome": 23275,
      "childreninsingleparenthouseholds": 33.224850811,
      "violentcrimerate": 941.43198334,
      "averagedailypm25": 9.1,
      "severehousingproblems": 15.347550638,
      "drivealonetowork": 83.470246386,
      "longcommutedrivesalone": 33.7
    },
    "longitude": -94.34749665503394,
    "latitude": 39.016701918102484,
    "education": {
      "lessthanhighschool": 9.4,
      "highschool": 28.3,
      "somecollege": 30.7,
      "bachelors": 31.6
    },
    "zipcodes": [
      "64137",
      "64111",
      "64053",
      "64055",
      "64064",
      "64029",
      "64106",
      "64108",
      "64034",
      "64118",
      "64136",
      "64139",
      "64125",
      "64030",
      "64014",
      "64066",
      "64080",
      "64123",
      "64131",
      "64145",
      "64128",
      "64121",
      "64170",
      "64050",
      "64057",
      "64133",
      "64109",
      "64130",
      "64134",
      "64129",
      "64158",
      "64163",
      "64070",
      "64102",
      "64105",
      "64086",
      "64101",
      "64124",
      "64157",
      "64088",
      "64061",
      "64051",
      "64002",
      "64081",
      "64013",
      "64016",
      "64112",
      "64114",
      "64110",
      "64152",
      "64127",
      "64147",
      "64120",
      "64146",
      "64199",
      "64058",
      "64054",
      "64074",
      "64119",
      "64138",
      "64149",
      "64156",
      "64132",
      "64171",
      "64148",
      "64141",
      "64999",
      "64052",
      "64015",
      "64063",
      "64075",
      "64056",
      "64082",
      "64113",
      "64155",
      "64126",
      "64197",
      "64065",
      "64198"
    ],
    "lifeexpectancy": 77.19,
    "avgincome": 47054,
    "povertyrate": 13.7,
    "costofliving": {
      "livingwage": 14.55,
      "foodcosts": 3246,
      "medicalcosts": 2681,
      "housingcosts": 8136,
      "taxcosts": 6263
    },
    "landareakm2": 1565.601892,
    "areakm2": 1596.319707
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`name`	string	`jackson county`	County name in lowercase format
`state`	string	`MO`	Two-letter state code where county is located
`age`Premium	object	`{…}`	Age distribution percentages by age groups
`age.0-4`Premium	number	`0.0644100874666257`	Percentage of population aged 0-4 years
`age.5-9`Premium	number	`0.06532756955438819`	Percentage of population aged 5-9 years
`age.10-14`Premium	number	`0.06595060390235714`	Percentage of population aged 10-14 years
`age.15-19`Premium	number	`0.05967616438434107`	Percentage of population aged 15-19 years
`age.20-24`Premium	number	`0.059725950234064615`	Percentage of population aged 20-24 years
`age.25-29`Premium	number	`0.08192474939936928`	Percentage of population aged 25-29 years
`age.30-34`Premium	number	`0.07523353119652466`	Percentage of population aged 30-34 years
`age.35-39`Premium	number	`0.06868029092005673`	Percentage of population aged 35-39 years
`age.40-44`Premium	number	`0.05831060964906666`	Percentage of population aged 40-44 years
`age.45-49`Premium	number	`0.058805623240603636`	Percentage of population aged 45-49 years
`age.50-54`Premium	number	`0.05858087569042305`	Percentage of population aged 50-54 years
`age.55-59`Premium	number	`0.0674783182624454`	Percentage of population aged 55-59 years
`age.60-64`Premium	number	`0.06208437705811146`	Percentage of population aged 60-64 years
`age.65-69`Premium	number	`0.05099777955110233`	Percentage of population aged 65-69 years
`age.70-74`Premium	number	`0.03891546504962227`	Percentage of population aged 70-74 years
`age.75-79`Premium	number	`0.026437708656052324`	Percentage of population aged 75-79 years
`age.80-84`Premium	number	`0.01748905778145719`	Percentage of population aged 80-84 years
`age.85+`Premium	number	`0.01997123800338828`	Percentage of population aged 85 years and older
`male`Premium	number	`339932`	Total male population count in county
`female`Premium	number	`363079`	Total female population count in county
`health`Premium	object	`{…}`	Health metrics and indicators for the county
`health.poorhealth`Premium	number	`20.588989742`	Percentage of population reporting poor health status
`health.physicallyunhealthydays`Premium	number	`4.247736361`	Average physically unhealthy days per month
`health.mentallyunhealthydays`Premium	number	`4.8111015035`	Average mentally unhealthy days per month
`health.lowbirthweightpercent`Premium	number	`9.1518749808`	Percentage of low birth weight births
`health.smokerspercent`Premium	number	`20.957241772`	Percentage of population that smokes
`health.obesitypercent`Premium	number	`31.5`	Percentage of population classified as obese
`health.foodenvindex`Premium	number	`7.5`	Food environment index rating for county
`health.physicallyinactivepercent`Premium	number	`23.2`	Percentage of physically inactive population
`health.excessivedrinkingpercent`Premium	number	`18.940103365`	Percentage of excessive alcohol drinkers
`health.alcoholimpaireddrivingdeaths`Premium	number	`152`	Annual alcohol-impaired driving deaths in county
`health.teenbirthrate`Premium	number	`31.109351559`	Birth rate among teenage population
`health.uninsured`Premium	number	`12.486314662`	Percentage of uninsured population
`health.withannualmammogram`Premium	number	`45`	Percentage receiving annual mammogram screening
`health.vaccinated`Premium	number	`51`	Percentage of vaccinated population
`health.childreninpoverty`Premium	number	`19.6`	Percentage of children living in poverty
`health.80thpercentileincome`Premium	number	`108296`	80th percentile income level in dollars
`health.20thpercentileincome`Premium	number	`23275`	20th percentile income level in dollars
`health.childreninsingleparenthouseholds`Premium	number	`33.224850811`	Percentage of children in single-parent households
`health.violentcrimerate`Premium	number	`941.43198334`	Violent crime rate per 100,000 people
`health.averagedailypm25`Premium	number	`9.1`	Average daily PM2.5 air pollution level
`health.severehousingproblems`Premium	number	`15.347550638`	Percentage with severe housing cost burden
`health.drivealonetowork`Premium	number	`83.470246386`	Percentage driving alone to work
`health.longcommutedrivesalone`Premium	number	`33.7`	Percentage with long commute driving alone
`longitude`Premium	number	`-94.34749665503394`	Geographic longitude coordinate of county
`latitude`Premium	number	`39.016701918102484`	Geographic latitude coordinate of county
`education`Premium	object	`{…}`	Educational attainment distribution for county
`education.lessthanhighschool`Premium	number	`9.4`	Percentage with less than high school education
`education.highschool`Premium	number	`28.3`	Percentage with high school education
`education.somecollege`Premium	number	`30.7`	Percentage with some college education
`education.bachelors`Premium	number	`31.6`	Percentage with bachelor's degree or higher
`zipcodes`Premium	array	`["64137","64111","64053"]`	Array of all ZIP codes within the county
`lifeexpectancy`	number	`77.19`	Average life expectancy in years
`avgincome`	number	`47054`	Average annual household income in dollars
`povertyrate`	number	`13.7`	Percentage of population below poverty line
`costofliving`Premium	object	`{…}`	Cost of living metrics for the county
`costofliving.livingwage`Premium	number	`14.55`	Living wage per hour in dollars
`costofliving.foodcosts`Premium	number	`3246`	Average annual food costs in dollars
`costofliving.medicalcosts`Premium	number	`2681`	Average annual medical costs in dollars
`costofliving.housingcosts`Premium	number	`8136`	Average annual housing costs in dollars
`costofliving.taxcosts`Premium	number	`6263`	Average annual tax costs in dollars
`landareakm2`Premium	number	`1565.601892`	Land area of county in square kilometers
`areakm2`Premium	number	`1596.319707`	Total area of county in square kilometers

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 10 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Mortgage Applicant Benchmarking: Mortgage lenders check local poverty rates and average household income to benchmark applicant earnings against county economic baselines.
Public Health Screening: To prioritize regional health initiatives, medical researchers compare county life expectancy figures with state poverty rates.
Retail Site Selection: When evaluating new storefront locations, commercial real estate developers review county average income and poverty levels to assess consumer spending capacity.
Grant Resource Allocation: Nonprofit program managers distribute community development grants by evaluating county poverty rates and average household income against national benchmarks.

Other ways to use US County Data Skill

Set up US County Data Skill on VerveKit, or reach the same source a different way. Your VerveKit account and credits work on all of them — one key, one balance.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →

Ground an agent on itA cited, machine-checkable fact your model can't produce on its own.VerveContext →Reference →