Agent Verifier — Independent Number Verification
Server Details
Verify totals in finished tables add up. Independent arithmetic audit for AI agents, signed receipt.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 1 tool
There is only one tool, so there is no possibility of selecting the wrong tool. Its purpose is clearly stated as independent arithmetic verification of finished tables and totals.
The single tool name follows a clear verb_noun pattern: verify_numbers. There are no competing conventions or inconsistent casing to confuse an agent.
A single tool is defendable for this very narrow purpose, but the surface is thin and offers no separate operations for different verification modes or outputs. It sits at the borderline of the 1-2 tool range.
The stated domain is arithmetic consistency checking of finished tables and line-item totals, and the tool covers row totals, column totals, subtotals, mismatches, recomputed values, differences, and a verification receipt. Source-data validation and expression evaluation are explicitly out of scope, so no obvious lifecycle gaps remain.
Available Tools
1 toolverify_numbersVerify numbers in a finished table or breakdownAInspect
Verify that the numbers in a finished table add up — before you deliver it. Use this when you have produced or received a table, spreadsheet, CSV/TSV or markdown table, a cost breakdown, invoice, quotation or budget, and need an independent check that row totals, column totals and subtotals are arithmetically consistent, or that a list of line items sums to a stated total. Returns every mismatched cell with the recomputed value and the difference, plus a signed verification receipt (verification ID, UTC time, SHA-256 content hash). Checks arithmetic consistency only; does not verify source data. Not a calculator — it does not evaluate expressions; it audits finished numbers.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The finished table (markdown, CSV or TSV) or the "name: value" breakdown text to audit. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and mostly does: it discloses the return payload ('every mismatched cell with the recomputed value and the difference, plus a signed verification receipt (verification ID, UTC time, SHA-256 content hash)') and two hard limits (arithmetic only, no source-data validation). It stops short of stating that content is not mutated or whether any permissions/quotas apply, which is the only remaining gap for a zero-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, front-loaded with the core action, then usage trigger, then return shape, then limits — each sentence contributes distinct information with no restatement of the title or schema. Dense but never padded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so describing the mismatch list and the signed receipt is necessary and is done here. Combined with the stated scope limits and the input format expectations, an agent has everything needed to decide to call it and to interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the single parameter's schema text already names markdown/CSV/TSV and the 'name: value' breakdown form. The description repeats those formats and adds only domain flavor (invoice, quotation, budget) without clarifying edge cases such as how totals must be labeled for detection to work, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Verify that the numbers in a finished table add up') and pins down scope: row totals, column totals, subtotals, line-item sums. It also explicitly separates itself from a calculator ('does not evaluate expressions; it audits finished numbers'), so the agent knows exactly what class of tool this is.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives a concrete triggering condition ('Use this when you have produced or received a table, spreadsheet, CSV/TSV or markdown table, a cost breakdown, invoice, quotation or budget') plus exclusions ('Checks arithmetic consistency only; does not verify source data'). No siblings exist, and the when/when-not framing is explicit enough that no inference is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
verify_numbers
Related MCP Connectors
Deterministic signed verification of numeric & financial claims for AI agents & spreadsheets.
Messy spreadsheets in, clean checkable tables out. Every result carries its arithmetic proof.
Statements, invoices, tables, ledgers — every result carries its own arithmetic proof.
Issue signed receipts for AI agent actions; verify any receipt offline - free, no account.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceDeterministic verification for AI-generated analysis. Reconciliation, consistency and Excel-integrity checks that stop the line when the numbers don't add up.1MIT
- AlicenseAqualityDmaintenanceMCP server for deterministic numeric verification and spreadsheet auditing. Enables AI agents to verify math/finance claims, audit rows, and evaluate covenant rules with signed, independently checkable receipts.4MIT
- AlicenseCqualityAmaintenanceEnables auditing of extracted balance sheets, income statements, and cash flow reports by reconstructing complex document tables and verifying mathematical consistency and formula balancing. It also performs cross-reference resolution and sensitive PII redaction, returning structured matrices and validation telemetry over MCP to clients like Claude Desktop, Cursor, and Windsurf.47MIT
- AlicenseBqualityAmaintenanceVerify a number before an agent asserts it — a Deflated Sharpe Ratio for backtest, plus eval-gap, subset-win, and judge-bias checks, with signed receipts anyone can verify offline.34MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.