Global Stock Market Skill

Overview

Index values come from the OECD share price series published on FRED, one monthly series per country, normalised so that the 2015 average equals 100 and not seasonally adjusted. A collector checks FRED every morning, keeps the newest reading as the current value and stores each year's months for history. `value` is that index level and `date` is the first day of the month it describes; `baseline` is always `2015=100`.

Without `year` and `month` you get the newest stored month. With them (paid plans) you get that exact month; `year` must be 2000 or later and not in the future, `month` must be 1 to 12, and sending `month` without `year` is rejected with a 400. A month that the series has not published yet returns a 404 rather than a nearby month.

`previousValue` and `previousDate` are the closest earlier stored month, and `changeDirection` compares the two. The paid `change1m` to `change12m` fields are percent changes against the reading exactly that many months earlier, rounded to four decimals, and are null when that month is missing. `changeYtd` is measured against the previous December, so for a January reading it equals `change1m`. `high12m`, `low12m` and `average12m` cover the twelve months ending at the returned one and need at least two stored readings. `history`, only on paid plans with `history=true`, lists those months oldest first.

Live Test Global Stock Market Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as GlobalStockMarketSkill. 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.

{
  "name": "GlobalStockMarketSkill",
  "arguments": {
    "country": "DE"
  }
}

You do not name the tool yourself; the model picks it. Asking about DE in the terms this skill covers is enough for it to reach for GlobalStockMarketSkill 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"
    }
  }
}

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.

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.

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{
  "status": "ok",
  "error": null,
  "data": {
    "country": "DE",
    "countryName": "Germany",
    "baseline": "2015=100",
    "year": 2026,
    "month": 9,
    "value": 159.6559,
    "date": "2026-09-01",
    "previousValue": 163.2341,
    "previousDate": "2026-08-01",
    "changeDirection": "down",
    "change1m": -2.1921,
    "change3m": 2.3115,
    "change6m": 6.7657,
    "change12m": 5.4324,
    "changeYtd": 3.4851,
    "high12m": 163.2341,
    "low12m": 149.5385,
    "average12m": 156.1535,
    "history": [
      {
        "date": "2025-10-01",
        "value": 154.4256
      },
      {
        "date": "2025-11-01",
        "value": 150.3501
      },
      {
        "date": "2025-12-01",
        "value": 154.2791
      },
      {
        "date": "2026-01-01",
        "value": 159.3243
      },
      {
        "date": "2026-02-01",
        "value": 160.134
      },
      {
        "date": "2026-03-01",
        "value": 149.5385
      },
      {
        "date": "2026-04-01",
        "value": 153.3461
      },
      {
        "date": "2026-05-01",
        "value": 156.074
      },
      {
        "date": "2026-06-01",
        "value": 156.0489
      },
      {
        "date": "2026-07-01",
        "value": 157.4312
      },
      {
        "date": "2026-08-01",
        "value": 163.2341
      },
      {
        "date": "2026-09-01",
        "value": 159.6559
      }
    ]
  }
}

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.

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.

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

Country Comparison

Compare stock market performance between countries like Germany vs France over the past decade

Economic Research

Analyze global market trends and correlations between national economies

Investment Analysis

Evaluate which country markets have outperformed or underperformed relative to 2015 baseline

Market Dashboards

Build global market overview dashboards showing performance across regions

Other ways to use Global Stock Market Skill

Set up Global Stock Market 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.

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