LogicNodes

Check numbers before you send them (correct / wrong + the right value)

verify_answer

Read-only

[FREE: 25 calls/day, then $0.01 USDC per call via x402] Before your agent sends a figure to a customer, a ledger or another agent, check it. Each claim is an expression and the value you believe it equals: plain arithmetic ("1299.99 * 3 * 1.0825" expected "4221.87") or over your rows ("sum([Amount])" expected "4312.50", with optional per-claim where filters). Answers per claim: "correct" (exactly equal, or within your tolerance), "correct_when_rounded" (equal once the true value is rounded half-up to the decimals you wrote), or "wrong", always with the exact value and the difference. Same exact engine as table_calc and decimal_calc; up to 50 claims per call, with a receipt you can attach as proof.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "claims": "<claims>"
    },
    "name": "verify_answer"
  }
}

Input Schema

Name	Required	Description
`csv`	No	the data as CSV text with a header row (instead of rows)
`asOf`	No	the date age_days() counts up to, YYYY-MM-DD (required for age_days: answers never silently depend on today's date)
`rows`	No	the data as JSON objects, one per row (up to 10000). Numbers as decimal strings are safest; JSON numbers are accepted
`where`	No	keep only rows matching ALL of these: eq, ne, gt, gte, lt, lte (numeric when both sides are numbers), contains (case-insensitive), in (list), empty, not_empty
`claims`	Yes
`delimiter`	No	CSV delimiter, default comma
`dateFormat`	No	how to read slash dates like 03/04/2026: mdy (US) or dmy; ISO dates (2026-03-04, with or without a time) are always read. Default iso
`strictNumbers`	No	true: refuse any cell that is not a plain decimal (no $, commas, %, or text in number columns). Default false: those are read and every change is reported

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 30, 2026

TDQS

A4.2/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the read-only and open-world annotations, it discloses pricing (25 free calls/day, then $0.01 USDC via x402), the per-call claim cap of 50, the exact answer vocabulary ('correct', 'correct_when_rounded', 'wrong'), the rounding rule (half-up to the decimals written), and that every answer returns the exact value and difference plus an attachable receipt. That is substantial behavioral context an agent cannot get from the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The trigger scenario is front-loaded and the pricing bracket is clearly delimited, then the expression format, answer values, and limits follow in a compact flow. It is dense and packs several ideas into long clauses, but every sentence contributes and nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does the needed work: it enumerates the possible answer statuses, states that the exact value and difference are always returned, and mentions the receipt. What remains thin is the interaction between tolerance and the 'correct_when_rounded' status, which an agent must infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 88%, so the schema already carries most parameter meaning, but the description adds real value: concrete expression syntax for both plain arithmetic and row aggregates, the notion of per-claim where filters layered on top-level filters, and how 'expected' is interpreted (tolerance, rounding to written decimals, $ and commas read). It does not cover csv/rows/asOf/dateFormat argument interactions, so it does not fully compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: check a claimed figure (an expression plus an expected value) before it is sent. It partially distinguishes itself from siblings by noting it runs the 'same exact engine as table_calc and decimal_calc', but it never says which of those three an agent should pick, so the differentiation is implicit rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is concrete: 'Before your agent sends a figure to a customer, a ledger or another agent, check it' names the trigger clearly. There are no explicit exclusions and no rule for choosing between this tool and table_calc/decimal_calc, which keeps it below a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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