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extract_claims

Split text into independently checkable atomic factual claims to prepare for verification. Returns claims, count, and a signed receipt.

Instructions

PURPOSE: Split text into independently checkable ATOMIC factual claims — the cheap first step of a verification loop (extract -> ground -> attest). Returns {claims, count, input_sha256} plus a signed receipt bound to the input hash. GUIDELINES: call when you want to see WHICH claims a document makes before paying to ground them, to budget a verification pass (extract everything, then verify_claim only the claims that matter to your decision), or to prove later exactly which claims were pulled from exactly which text (the receipt binds both). Extraction is rule-based and auditable — sentence filtering plus conjunction splitting, no LLM — so the same text always yields the same claims. Use check_citations when you want extraction AND grounding in one call. PARAMETERS: text = the prose to decompose; maxClaims 1-50 (default 20). LIMITATIONS: extracts declarative factual sentences; skips questions, opinions, instructions, first-person statements; splits only on high-precision conjunction boundaries so under-splitting is possible. Does NOT verify anything. Paid per call on the hosted engine (x402, cheapest tool); free on a local engine. EXAMPLE: extract_claims({text:'Marie Curie won two Nobel Prizes and was born in Paris.'}) -> {count: 2, claims: ['Marie Curie won two Nobel Prizes', 'was born in Paris.']}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to split into independently checkable atomic factual claims.
maxClaimsNoMax claims to return (1-50).
Behavior5/5

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

No annotations exist, so the description carries full burden. It discloses rule-based, deterministic extraction, limitations (skips non-factual statements, under-splitting), and payment model. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (PURPOSE, GUIDELINES, PARAMETERS, LIMITATIONS, EXAMPLE). Every sentence adds value, front-loaded with purpose and usage.

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?

Complete for a 2-parameter tool without output schema. Explains return format, limitations, example, and payment context. No gaps.

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 coverage is 100% and description adds usage context (maxClaims range, default, example). Minimal extra semantics beyond schema, so slight deduction from perfect.

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 explicitly states the purpose as splitting text into atomic factual claims for verification loops. It uses specific verbs and resources and distinguishes from sibling tool check_citations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear guidance on when to use (e.g., before grounding, for budgeting, proving claims) and explicitly names check_citations as an alternative for combined extraction and grounding.

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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