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Check Understanding

check

Validate your understanding against human intent and record alignment checks, with integrated Bayesian decision trails for tracking confidence and evidence.

Instructions

[MID-SESSION — safe any time; for alignment, before risky decisions] Use when the user asks to validate understanding, verify alignment, or check if their interpretation matches the human's intent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNoThe goal or decision question you're checking alignment on. Required for alignment checks; optional when recording a pure decision trail (prior/posterior/evidence).
understandingNoAlias for goal — use when saying 'check my understanding: X'. Provide either goal or understanding.
confidenceNoHow confident you are. Defaults to medium.medium
assumptionsNoKey assumptions you're making.
human_correctionNoAfter human responds: what they actually wanted (or 'confirmed').
deltaNoThe gap between your understanding and reality (or 'none').
projectNoauto
priorNoInitial probability estimate (0-1). Start of Bayesian decision trail.
evidenceNoEvidence collected since prior. Each entry shifts probability.
posteriorNoUpdated probability after considering evidence (0-1).
outcomeNoFinal decision result: 'confirmed', 'rejected', 'partial', or free text. Triggers decision trail persistence.
decision_idNoLink multiple check calls to the same decision. Auto-generated if not provided.
Behavior2/5

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

No annotations provided, so description must disclose behavioral traits. Only says 'safe any time' but does not explain side effects, authorization needs, or that it may persist decision trail data. Significant gap.

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?

Single, front-loaded sentence with a contextual tag. No wasted words; every part serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

High complexity with 12 parameters and no output schema. Description only covers usage context, omitting tool behavior, return values, and decision trail functionality. Incomplete.

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?

Schema description coverage is 92%, so baseline 3 applies. Description does not add any parameter meaning beyond schema, which already provides detailed descriptions for all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states it validates understanding, verifies alignment, or checks interpretation match with human intent. Provides context 'mid-session, safe any time' but does not explicitly distinguish from siblings like recall or remember.

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?

Clearly says when to use: when user asks to validate understanding or verify alignment. Context 'before risky decisions' adds helpful guidance. Does not list alternatives or when to avoid.

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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