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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

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only/idempotent, so the description adds valuable context beyond those: the could_not_verify vs. unsupported distinction, the verification_error object, and the automatic fallback pipeline. This meaningfully enriches the agent's understanding of edge cases.

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 dense and front-loaded with usage patterns, followed by how the routing works and warnings. Some text could be sectioned for easier scanning, but every sentence carries relevant information and there is no fluff.

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 must explain the return shape, and it does: verdict list, grounded/structured value with citation, reasoning, and the error semantics for could_not_verify vs. unsupported. It also mentions the efficiency benefit (replacing 4–6 sequential calls), making it complete for a verification tool.

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?

The input schema already has detailed descriptions for both parameters (100% coverage). The description adds extra semantics for tolerance_pct (overriding implied wording, default cap at 5) and provides realistic examples for claim, enhancing the schema rather than repeating it.

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 defines a specific verb ('validate') and resource ('natural-language claims') with explicit trigger phrases and output verdicts. It clearly distinguishes a fact-checking tool from generic lookup tools, and the example intents ("fact check", "verify the claim that…") make the purpose unmistakable.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing for company-financial vs. any other factual claim. It lacks formal when-not guidance or named alternative tools, but the context is unambiguous and operational.

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 with clear descriptions, but ask_pipeworx_beta currently duplicates ask_pipeworx, and the multiple prediction market tools could be confusing without careful reading.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun or noun_verb pattern, with no mixing of conventions.

Tool Count3/5

34 tools is high for a single server, covering chain data, Pipeworx research, and prediction markets. While well-organized, the breadth pushes the boundary of manageable scope.

Completeness4/5

The tool set covers major query operations for chains, entities, and data sources, with subscription and memory features. Minor gaps like lack of chain creation are acceptable given the server's focus on data retrieval.

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