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grounded_verify

Reality-check a claim before acting on it: live web search and calibrated analysis yield a supported/unsupported/uncertain verdict with confidence and cited evidence.

Instructions

Web-GROUNDED reality-check on a CLAIM — the default product. PRICE: ~$0.25 per call (x402, USDC on Base mainnet). Runs a live, capped web search and a calibrated Sonnet synthesis — use it right before an agent acts on, repeats, or surfaces a fact that matters (a fabricated citation, an invented policy, a wrong number). Escalate to pro_verify for the deepest synthesis.

Returns: verdict (supported | unsupported | uncertain), an honest 0-1 confidence, reasoning,
cited evidence, and (when signing is enabled) a signed Ed25519 receipt (re-check for free with
verify_receipt). Abstains rather than guess; never fabricates citations.

Pricing is DISCLOSED and paid per use via x402 — Verity holds no key and never charges silently.
Optional affiliate_id tags the call for a future referral program; it never changes price or behavior.
(Independent fact-verification; maps to OWASP LLM/ASI guidance on grounding — produces an audit artifact.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYes
contextNo
affiliate_idNo
Behavior5/5

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

Despite having no annotations, the description discloses pricing, payment mechanism, abstention behavior, no-citation-fabrication guarantee, return values, and the affiliate_id's non-impact. This is rich behavioral context that goes far beyond the schema.

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 well-structured with clear sections, but the concluding OWASP/audit-artifact note is tangential. It could be trimmed without losing essential information.

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?

The description covers purpose, returns, pricing, alternatives, and safety behavior. However, it omits explanation of the context parameter and does not mention quick_verify for tier comparison, leaving a small gap in completeness.

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 description explains the claim parameter implicitly and gives a detailed note on affiliate_id, but it does not describe the context parameter at all. With zero schema coverage, this leaves the agent without guidance on what context means.

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 states the tool is a web-grounded reality-check on a claim, naming it the default product. It specifies the exact output and behavior, and distinguishes itself by instructing escalation to pro_verify, leaving no ambiguity about the tool's purpose.

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 says to use it right before an agent acts on, repeats, or surfaces a fact that matters, and advises escalating to pro_verify for deeper synthesis. However, it does not compare with quick_verify, leaving some ambiguity about when to choose the lighter sibling.

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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