Cost of Living Grounding Data

Overview

Cost of Living uses regional Consumer Price Index data from the Bureau of Labor Statistics to calculate relative cost indices. The national average is set to 100, so an index of 120 means 20% more expensive than average. Data is aggregated by Census region (Northeast, Midwest, South, West) for large metro areas.

Live Test Cost of Living Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as CostofLivingGroundingData. 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": "CostofLivingGroundingData",
  "arguments": {
    "location": "California"
  }
}

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

Connecting

One server URL covers every source 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": {
    "vervecontext": {
      "url": "https://api.vervecontext.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
`location`Required	string	State name, state code (e.g., CA, NY), or major city name.

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": {
    "searchedLocation": "California",
    "region": "west-large",
    "regionName": "West Large Metros (LA, SF, Seattle, Phoenix, Denver area)",
    "costIndex": 107,
    "description": "7% above average",
    "cpi": 356.993,
    "usAverageCPI": 334.98,
    "period": "2026-08",
    "note": "Cost index is relative to US average (100). Values above 100 indicate higher than average cost of living."
  }
}

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
`searchedLocation`	string	`California`	The queried location name or code provided
`region`Premium	string	`west-large`	Region identifier (e.g., west-large, south-large)
`regionName`Premium	string	`West Large Metros (LA, SF, Seattle, Phoenix, Denver area)`	Full region name with major cities included
`costIndex`	number	`107`	Cost of living index (100 is national average)
`description`	string	`7% above average`	Plain-language summary of how the location compares to the US average
`cpi`	number	`356.993`	Consumer price index for the location
`usAverageCPI`	number	`334.98`	Consumer price index for the US as a whole, for comparison
`period`	string	`2026-08`	Month the index values are for, as YYYY-MM
`note`	string	`Cost index is relative to US average (100). Values above 100 indicate higher than average cost of living.`	How to read the index: 100 is the US average, above 100 is more expensive

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at searchedLocation: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

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 source is not part of VerveContext. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Relocation Planning: Compare cost of living when considering job offers in different states or cities
Salary Negotiations: Calculate salary equivalents across different regions
Business Location: Factor cost of living into business location and expansion decisions
HR Planning: Set location-adjusted compensation for remote or distributed teams

Other ways to use Cost of Living Grounding Data

Set up Cost of Living Grounding Data on VerveContext, or reach the same source a different way. Your VerveContext 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 →

Give it to an AI agentConnect over MCP and your agent calls it as a native tool — Claude, Cursor, ChatGPT.VerveKit →Reference →

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