Purchasing Power Grounding Data

Overview

Purchasing Power uses official CPI data from the Bureau of Labor Statistics to calculate accurate inflation adjustments. Historical data is available back to 1947, enabling long-term purchasing power comparisons.

Live Test Purchasing Power Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as PurchasingPowerGroundingData. 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": "PurchasingPowerGroundingData",
  "arguments": {
    "amount": "100",
    "from": "1990"
  }
}

You do not name the tool yourself; the model picks it. Asking about 100 in the terms this source covers is enough for it to reach for PurchasingPowerGroundingData 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
`amount`Required	number	The dollar amount to convert range 0–∞
`from`Required	string	Starting year (YYYY format, data available from 1947) length 4–4 · year
`to`Optional	string	Ending year (YYYY format). Omit for current. length 4–4 · year

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": {
    "originalAmount": 100,
    "originalPeriod": "1990-01",
    "adjustedAmount": 242.31,
    "adjustedPeriod": "2024-01",
    "cumulativeInflation": 142.31,
    "multiplier": 2.423,
    "explanation": "$100 in 1990-01 has the same purchasing power as $242.31 in 2024-01",
    "fromCPI": 127.4,
    "toCPI": 308.417
  }
}

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
`originalAmount`	number	`100`	The dollar amount you provided, before any inflation adjustment
`originalPeriod`	string	`1990-01`	The starting period the original amount is valued in, normalized to YYYY-MM
`adjustedAmount`	number	`242.31`	The dollar amount with the same purchasing power as the original amount, expressed in the ending period
`adjustedPeriod`	string	`2024-01`	The ending period the adjusted amount is valued in, normalized to YYYY-MM
`cumulativeInflation`Premium	number	`142.31`	The total percentage change in prices between the starting and ending periods
`multiplier`Premium	number	`2.423`	The factor by which prices changed between the two periods; multiplying the original amount by this yields the adjusted amount
`explanation`	string	`$100 in 1990-01 has the same purchasing power as $242.31 in 2024-01`	A human-readable sentence summarizing the conversion
`fromCPI`Premium	number	`127.4`	The Consumer Price Index value for the starting period
`toCPI`Premium	number	`308.417`	The Consumer Price Index value for the ending period

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 originalAmount: 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

Salary Comparison: Compare salaries across different years in real purchasing power terms
Investment Returns: Calculate real (inflation-adjusted) returns on investments
Historical Analysis: Understand the true value of historical prices and costs
Contract Adjustments: Calculate inflation adjustments for long-term contracts

Other ways to use Purchasing Power Grounding Data

Set up Purchasing Power 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 →