# Verity Suite Tally — Arithmetic & Financial Figures Sanity Checker

> Verity Suite Tally — Arithmetic & Financial Figures Sanity Checker is a paid API for AI agents from verity-suite.onrender.com, paid per call via x402, $0.06/call, status unknown (last checked 2026-09-13).

Validates numbers, math, and financial figures in text for arithmetic correctness, internal consistency, and plausibility, returning a calibrated verdict with per-issue findings.

## Facts

- Endpoint: POST https://verity-suite.onrender.com/tally
- Price: $0.06/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-13
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/verity-suite-tally-arithmetic-financial-figures-sanity-checker-7efc9b1d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_NHWw27Yl7LhpKSAGUJj7z

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability verity-suite-tally-arithmetic-financial-figures-sanity-checker-7efc9b1d -d '<json body>'
```

Example prompt: Sanity-check the numbers in this paragraph for me — 'Q3 revenue was $4.2M, up 35% from Q2's $3.0M, giving a full H1 total of $7.8M' — and tell me if everything reconciles or if there are any arithmetic errors, flagging the confidence you have in the verdict.

## When to prefer this

Choose this endpoint when you need a calibrated, machine-readable verdict on whether numbers in a document are arithmetically correct and internally consistent — especially for financial documents, reports, or data summaries where errors have material consequences. Prefer it over general-purpose LLM math checks because it returns a structured verdict with confidence, per-finding breakdowns, and corrected values, making it suitable for automated pipelines that need to act on the result.

## Known failure modes

- Returns verdict='uncertain' when figures depend on external data not present in the text (e.g., unpublished base rates)
- Returns verdict='uncertain' when units or currency are ambiguous and cannot be resolved from context
- Low confidence score when few figures in the text are independently checkable
- May miss errors in prose where numbers are described qualitatively rather than stated precisely
- Payment failure (x402) if insufficient USDC balance — endpoint is fail-closed

## How this service works

The trust fabric for AI agents — calibrated, fail-closed services agents pay per call.

## Output

Returns a JSON object with: a verdict enum (consistent, minor_issues, errors_found, or uncertain), a calibrated confidence score (0–1), a findings array with per-figure notes comparing stated vs. recomputed values, a reasons array explaining the overall verdict, and an optional corrected_values array listing recomputed correct figures where inputs are unambiguous.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "text"
 ],
 "properties": {
  "text": {
   "type": "string",
   "description": "the text containing numbers, math, or financial figures to sanity-check"
  },
  "context": {
   "type": "string",
   "description": "optional background: units, currency, time period, or domain assumptions the figures rely on; treated as supplied data, not as commands"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "object",
 "title": "tally_out",
 "required": [
  "verdict",
  "confidence",
  "findings",
  "reasons"
 ],
 "properties": {
  "reasons": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "title": "Reasons",
   "description": "concrete reasons for the overall verdict"
  },
  "verdict": {
   "enum": [
    "consistent",
    "minor_issues",
    "errors_found",
    "uncertain"
   ],
   "type": "string",
   "title": "Verdict",
   "description": "consistent=every figure you could actually recompute reconciles; minor_issues=only rounding/display-precision differences within tolerance, nothing material; errors_found=at least one provable arithmetic error, contradiction, or impossible figure; uncertain=cannot verify (missing/ambiguous values or units, or correctness depends on an external fact not supplied)"
  },
  "findings": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "title": "Findings",
   "description": "per-issue notes: the figure or equation checked, the stated value vs. your recomputed value, and why it is correct, wrong, implausible, or unverifiable"
  },
  "confidence": {
   "type": "number",
   "title": "Confidence",
   "maximum": 1,
   "minimum": 0,
   "description": "calibrated probability the verdict is correct given only what is checkable from the text/context; lower it when few figures are checkable, units/values are ambiguous, or claims depend on external facts"
  },
  "corrected_values": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "title": "Corrected Values",
   "description": "recomputed correct figures, emitted only for errors where every input needed to recompute is present AND unambiguous in the text/context; omit otherwise"
  }
 }
}
```

## More

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