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finding_propose

Propose a finding with citations for automatic corroboration against archived evidence, ensuring verdict integrity and reducing false claims.

Instructions

Propose a finding. The corroboration engine independently verifies it against archived tool output before the ledger records a verdict.

claim_type tells the engine what kind of claim this is: "observation" - a direct read of primary evidence (a value literally present in an artifact, e.g. a recipient address in a POST body or a process name in pslist). Confirmed when the value is really in the cited output. "inference" - an analytical conclusion (attribution, causation, intent). Confirmed only when independent sources agree; otherwise labeled an inference. Use this for anything that combines observations rather than quoting one.

Each citation is a dict: {exec_id, artifact, locator, asserted_value}. asserted_value is the exact string the claim depends on; the engine checks it is really in that execution's output. If the verdict comes back UNSUPPORTED, the claim is a likely hallucination: do not present it as fact, investigate further, then revise. If it comes back CONTRADICTED, an independent source disagrees: resolve it before it stands.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hypothesis_idYes
statementYes
citationsYes
claim_typeNoinference
conflicting_citationsNo
confidenceNomedium

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses the corroboration engine's behavior, claim types, citation format, and expected verdicts, including guidance on handling outcomes.

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?

The description is well-structured, starting with purpose, then claim types, citation format, and verdict handling. Every sentence adds value without redundancy.

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?

Given the complexity (6 parameters, nested citations) and presence of output schema, the description covers key behavioral aspects but misses some parameter details like 'confidence'.

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?

Despite 0% schema coverage, the description explains 'claim_type' with its values and the citation dict format. However, it does not explain 'hypothesis_id', 'confidence', or 'conflicting_citations'.

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 'Propose a finding' and explains the corroboration engine workflow, distinguishing it from siblings like 'finding_revise' and 'corroborate_claim'.

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 provides context on when to propose a finding with citations and claim type, and advises on handling UNSUPPORTED or CONTRADICTED verdicts. However, it does not explicitly exclude alternatives.

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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