Trade Balance Grounding Data for AI Agents

Overview

Trade Balance sources data from FRED (Federal Reserve Economic Data) using the Census Bureau FT-900 Balance of Payments series, providing seasonally adjusted monthly exports, imports, and the trade balance in millions of US dollars, with history back to 1992. With changes=true every reading includes the previous month's balance, and paid plans add the percent change in exports and imports from the month before and the year before, the change in the balance, rolling 12-month exports, imports and balance, and the 12 monthly readings with history=true. Track the US balance of trade, the trade deficit or surplus, and imports and exports month by month.

Live Test Trade Balance Grounding Data for AI Agents Source →

The tool

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

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"
    }
  }
}

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.

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": {
    "yearMonth": "2026-08",
    "year": 2026,
    "month": 8,
    "exports": 315182,
    "imports": 420754,
    "balance": -105572,
    "unit": "millions_usd",
    "exportsFormatted": "$315.18 billion",
    "importsFormatted": "$420.75 billion",
    "balanceFormatted": "-$105.57 billion",
    "tradeStatus": "deficit",
    "updatedAt": "2026-10-28T03:39:54.665Z",
    "previousYearMonth": "2026-07",
    "previousBalance": -92826,
    "exportsChange1m": 1.435,
    "importsChange1m": 4.2632,
    "balanceChange1m": -12746,
    "exportsChange12m": 10.6908,
    "importsChange12m": 22.1762,
    "balanceChange12m": -45930,
    "exports12m": 3697470,
    "imports12m": 4491225,
    "balance12m": -793756,
    "history": [
      {
        "yearMonth": "2025-09",
        "exports": 292352,
        "imports": 351743,
        "balance": -59392
      },
      {
        "yearMonth": "2025-10",
        "exports": 301158,
        "imports": 338534,
        "balance": -37376
      },
      {
        "yearMonth": "2025-11",
        "exports": 289504,
        "imports": 353372,
        "balance": -63868
      },
      {
        "yearMonth": "2025-12",
        "exports": 285282,
        "imports": 361360,
        "balance": -76078
      },
      {
        "yearMonth": "2026-01",
        "exports": 301271,
        "imports": 353941,
        "balance": -52670
      },
      {
        "yearMonth": "2026-02",
        "exports": 313628,
        "imports": 366127,
        "balance": -52500
      },
      {
        "yearMonth": "2026-03",
        "exports": 321163,
        "imports": 374820,
        "balance": -53657
      },
      {
        "yearMonth": "2026-04",
        "exports": 329893,
        "imports": 382776,
        "balance": -52883
      },
      {
        "yearMonth": "2026-05",
        "exports": 319979,
        "imports": 395731,
        "balance": -75752
      },
      {
        "yearMonth": "2026-06",
        "exports": 317335,
        "imports": 388517,
        "balance": -71182
      },
      {
        "yearMonth": "2026-07",
        "exports": 310723,
        "imports": 403550,
        "balance": -92826
      },
      {
        "yearMonth": "2026-08",
        "exports": 315182,
        "imports": 420754,
        "balance": -105572
      }
    ]
  }
}

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.

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 yearMonth: 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.

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

Economic Analysis

Track US trade patterns for economic research

Policy Research

Analyze trade data for policy research and analysis

Business Intelligence

Factor trade trends into business planning

Other ways to use Trade Balance Grounding Data for AI Agents

Set up Trade Balance Grounding Data 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.

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