Docs/Sources/Shoe Size Conversion for AI Agents

Shoe Size Conversion for AI Agents

Convert shoe sizes between international standards

OperationalCredits 2 per callp50 230msReference Data

Overview

Shoe Size Converter works by converting the input size to centimeters as a base measurement, then calculating equivalent sizes in all supported regions. Returns conversions for all international standards simultaneously.

Live Test Shoe Size Conversion for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as ShoeSizeConversionforAIAgents. 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": "ShoeSizeConversionforAIAgents",
  "arguments": {
    "size": "9",
    "from": "us"
  }
}

You do not name the tool yourself; the model picks it. Asking about 9 in the terms this source covers is enough for it to reach for ShoeSizeConversionforAIAgents 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.

ArgumentTypeDescription
sizeRequiredstringThe shoe size to convert
fromRequiredstringSource region
usukeucmjpaumxkr
genderOptionalstringGender sizing
menwomenunisexchild
default unisex

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": {
    "input_size": 9,
    "input_region": "US",
    "gender": "men",
    "conversions": {
      "cm": 29.1,
      "jp": 29.1,
      "us": 9,
      "uk": 8.1,
      "au": 8.1,
      "eu": 43.7,
      "mx": 13.7,
      "kr": 43.7
    },
    "note": "Sizes are approximate and may vary by manufacturer"
  }
}

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.

FieldTypeExampleDescription
input_sizenumber9The size that was supplied
input_regionstringUSSizing system the input was given in
genderstringmenGender sizing used for conversion
conversionsobject{…}The equivalent size in every supported sizing system
conversions.cmPremiumnumber29.1Size in centimeters
conversions.jpPremiumnumber29.1Japanese shoe size
conversions.usnumber9US shoe size
conversions.uknumber8.1UK shoe size
conversions.aunumber8.1Equivalent Australian size
conversions.euPremiumnumber43.7EU shoe size
conversions.mxnumber13.7Equivalent Mexican size
conversions.krnumber43.7Equivalent Korean size, in millimetres
notestringSizes are approximate and may vary by manufacturerCaveat that sizes are approximate and vary by manufacturer

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 input_size: 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.

StatusWhat it means
400 / 422The arguments did not validate. The message names the offending one.
401The OAuth session is invalid or expired — reconnect the server.
403Blocked by a key restriction or an IP allow-list. Never a bad identity.
404This source is not part of VerveContext. Check the catalog.
429Out 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

E-commerce
Help international customers find their correct shoe size when shopping from different countries
Travel Planning
Convert shoe sizes when shopping abroad or purchasing from international retailers
Size Charts
Generate comprehensive size charts for footwear products with multiple regional standards
Retail Apps
Integrate size conversion features into shopping apps to improve customer experience

Other ways to use Shoe Size Conversion for AI Agents

Set up Shoe Size Conversion for AI Agents 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 →

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