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Verify a claimed statistic against the official source

verify_stat
Read-onlyIdempotent

Verify economic claims against official World Bank, IMF, ECB data. Receive verdict, official value, error diagnostics, and citation to prevent publishing wrong statistics.

Instructions

Check a claimed economic figure (from a draft, article, or memory) against the official statistical series and get a verdict: match, close, mismatch, or cannot_verify — with the official value, the difference, diagnostics for classic errors (wrong year, percent-vs-decimal, unit scaling), and a full citation for the correct number. Honesty contract: when the official source cannot support a judgment (source down, no published value, fallback vintage risk), the verdict is cannot_verify with the reason — never a guess. Supports historical IMF-vintage checks via as_of. Use this before publishing any economic statistic in a report, brief, or article.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoHistorical IMF-vintage verification: judge the claim against the dated IMF WEO edition resolved from this date — e.g. '2019-04', '2019' — instead of today's live data. Resolution is a conservative month calendar (editions flip May 1 / Nov 1, not the IMF's exact release days) and always verifies against a dated WEO edition even when the indicator's live primary is World Bank WDI or the IMF Fiscal Monitor — the response's as_of object and notes disclose both, so present results as 'matched the IMF WEO {vintage} edition', never 'was true at the time'. Recent editions serve from the IMF's own dated vintage dataflows (api.imf.org) directly, with DBnomics's dated editions as the deep archive — the citation names which one served. Only supported for the six WEO-dated registry indicators (gdp_growth, current_account_gdp, govt_debt_gdp, fiscal_balance_gdp, govt_revenue_gdp, govt_expenditure_gdp); rejects with advice otherwise, and impossible calendar dates are rejected.
periodYesPeriod of the claim, usually a year: '2024'.
countryNoCountry for registry indicators / World Bank series.
indicatorYesRegistry key ('inflation_cpi', 'gdp_growth', …) or explicit series id ('worldbank/FP.CPI.TOTL.ZG', 'imf/NGDP_RPCH', 'dbnomics/IMF/WEO:latest/USA.NGDP_RPCH.pcent_change').
claimed_valueYesThe value as claimed (in the series' own units).
strict_sourceNoReproducibility mode: never verify against a fallback source — error instead if the primary source fails. Default false; without it, a verify served from a transient-failure fallback returns cannot_verify with the fallback value as indicative (fallback_used=true), while a fallback for a series the primary permanently lacks (e.g. Taiwan in WDI) is judged normally with disclosure.
tolerance_absNoOptional absolute tolerance in series units (e.g. 0.1 percentage points).
tolerance_pctNoOptional relative tolerance in percent.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesYes
periodYes
seriesYes
verdictYes
citationYes
differenceYes
diagnosticsYes
explanationYes
claimed_valueYes
is_projectionYes
status_methodYes
official_valueYes
observation_statusYes
relative_difference_pctYes
Behavior5/5

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

The description goes far beyond the annotations (readOnly, idempotent, openWorld) with a detailed 'honesty contract' (cannot_verify rather than guessing), fallback behavior (strict_source mode, fallback_used=true), and vintage resolution semantics. It discloses exactly how verification failures are handled and what the response will disclose, which is more than the annotations provide.

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 description is long and rich, but every sentence carries meaningful behavioral or usage guidance. It is structured into a core purpose, a contract clause, and contextual notes. Though a single dense paragraph, it remains reasonably scannable and the length is justified for a tool with this complexity and safety-related behavior.

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

Completeness5/5

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

With 8 parameters, sibling tools, an output schema, and annotations, this description covers all key aspects: purpose, verdict types, diagnostics, honesty policy, fallback modes, as_of constraints, and the four classic errors. The presence of an output schema means the description need not detail return fields, so the completeness is excellent.

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

Parameters3/5

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

The input schema already has 100% description coverage for all 8 parameters, including detailed explanations for as_of, tolerance, and strict_source. The tool description itself adds minimal parameter-level semantic enhancement beyond referencing as_of and the registry indicator set; the schema carries the main weight, so a baseline 3 is appropriate.

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

Purpose5/5

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

The description opens with a specific verb ('Check') plus a clear resource ('claimed economic figure') and a defined outcome (verdict categories with official value, difference, diagnostics, citation). It is easily distinguished from sibling tools like 'get_series' or 'search' because it focuses on verification against an official source, and explicitly ties it to 'before publishing any economic statistic'.

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?

It states when to use: 'Use this before publishing any economic statistic in a report, brief, or article.' It also provides clear limitations (only six WEO-dated indicators for as_of, strict_source fallback behavior). However, it does not explicitly name alternatives like verify_claims or compare_sources, so it misses a direct when-not-to-use contrast.

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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