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Cnb Cz

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds critical behavioral context beyond those hints: the exact meaning of 'could_not_verify' (not evidence), the distinction between 'unsupported' and 'could_not_verify', and the two routing paths. This prevents serious misuse and is exactly the kind of disclosure needed.

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 longer than average, but nearly every sentence earns its place: trigger, pipeline routing, verdicts, caller warnings, and efficiency claim. It is front-loaded with the core purpose and uses quoted example phrases effectively. A small amount of redundancy (e.g., restating 'natural-language' while quoting examples) prevents a perfect score.

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 no output schema, the description thoroughly explains return verdicts, includes a citation format, and covers error semantics. It also distinguishes between financial and non-financial paths and addresses ambiguous outcomes like 'could_not_verify' and 'unsupported'. For a complex tool, this is remarkably complete.

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 description coverage is 100% with rich detail for both 'claim' and 'tolerance_pct', including examples, ranges, and defaults. The description adds contextual value by referring to 'exact percent-delta math' and the tolerance implications of claim wording, but the schema already carries most of the parameter burden, so a slightly above-baseline score 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 clearly identifies the tool as a natural-language claim verification tool with a specific verb ('validate') and resource ('claim'), and immediately differentiates it from general Q&A siblings by framing it as fact-checking user statements against authoritative sources.

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?

The description gives an explicit trigger ('use whenever the agent needs to check whether something a user said is factually correct') and explains the routing for company-financial vs other claims. However, it does not explicitly name alternative tools to prefer or state when not to use it, so it falls just short of full guidance.

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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TDQS

A4.2/5.0
Disambiguation4/5

Tools have distinct purposes overall, but some pairs (e.g., ask_pipeworx vs ask_pipeworx_grounded, entity_profile vs recent_changes) could cause agent confusion without careful reading. Descriptions mitigate overlap, so only minor ambiguity.

Naming Consistency4/5

Most tools use snake_case with verb_noun or noun_verb patterns, but several single-word verbs (forget, recall, remember, subscribe) and a few inconsistent forms (czeonia, pribor) break uniformity. Still, the pattern is largely predictable.

Tool Count4/5

31 tools is high but justified for a multi-domain data gateway covering finance, economics, FDA, betting, and subscriptions. Each tool has a clear role, though the count pushes the upper bound for easy scanning.

Completeness5/5

The tool set comprehensively covers its stated domains: entity resolution, financial data, exchange rates, interest rates, SEC filings, FDA, Polymarket analysis, subscriptions, and utility memory. No obvious gaps given its purpose as a data retrieval and analysis server.

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