Earnings Grounding Data for AI Agents

Overview

Earnings Report works by aggregating and normalizing SEC EDGAR financial filings (10-Q and 10-K) for US public companies into one combined view. The data includes revenue, net income, EPS, assets, liabilities, cash flow and margins. Use this endpoint when you want the full picture in one request; use the dedicated Income Statement, Balance Sheet, Cash Flow or Financial Ratios APIs when you only need one statement. Data is refreshed continuously from official SEC filings.

Live Test Earnings Grounding Data for AI Agents Source →

The tool

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

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

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": {
    "ticker": "ADBE",
    "company": "ADOBE INC.",
    "cik": "0000796343",
    "fiscalYear": 2026,
    "fiscalQuarter": 2,
    "filingType": "10-Q",
    "filingDate": "2026-06-15",
    "periodEnd": "2026-05-29",
    "income": {
      "revenue": 6618000000,
      "costOfRevenue": 715000000,
      "grossProfit": 5903000000,
      "operatingIncome": 2238000000,
      "netIncome": 1712000000,
      "eps": 4.25,
      "epsBasic": 4.26,
      "sharesOutstanding": 402500000,
      "sharesOutstandingBasic": 402400000,
      "researchAndDevelopment": null,
      "sellingAndMarketing": 1884000000,
      "sellingGeneralAndAdmin": null,
      "generalAndAdmin": 546000000,
      "interestExpense": null,
      "incomeTax": 526000000,
      "depreciation": 367000000,
      "stockBasedCompensation": 1043000000
    },
    "balance": {
      "totalAssets": 29933000000,
      "currentAssets": 9068000000,
      "cash": 4919000000,
      "receivables": 1993000000,
      "inventory": null,
      "propertyAndEquipment": 1870000000,
      "goodwill": 14041000000,
      "intangibles": 1012000000,
      "totalLiabilities": 18415000000,
      "currentLiabilities": 12078000000,
      "accountsPayable": 499000000,
      "longTermDebt": 4802000000,
      "equity": 11518000000,
      "retainedEarnings": 48767000000
    },
    "cashFlow": {
      "operatingCashFlow": 5123000000,
      "capitalExpenditures": 95000000,
      "freeCashFlow": 5028000000,
      "investingCashFlow": -1240000000,
      "financingCashFlow": -4399000000,
      "dividendsPaid": null,
      "shareRepurchases": 4589000000
    },
    "dividends": null,
    "lastUpdated": "2026-07-17T23:01:47.364Z",
    "metrics": {
      "grossMargin": 89.2,
      "operatingMargin": 33.82,
      "netMargin": 25.87,
      "revenueFormatted": "$6.62B"
    }
  }
}

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 company: 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 6 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Equity Research Portals

Equity research portals screen public stocks by pairing basic earnings per share, gross profit, and operating income to highlight firms with rising operational profitability.

Corporate Loan Underwriting

When evaluating commercial borrowers, underwriters inspect cash balances, current liabilities, and accounts payable to verify short-term liquidity before approving revolving credit lines.

Quarterly News Alerts

To publish quarterly market roundups, news desks extract cost of revenue, research spending, and share repurchase totals as soon as corporate filings clear.

Vendor Solvency Audits

Audit critical suppliers by checking inventory holdings, capital expenditure commitments, and dividend declarations before negotiating multi-year procurement deals.

Other ways to use Earnings Grounding Data for AI Agents

Set up Earnings 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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