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allenc84
by allenc84

log_assessment

Record forward-looking assessments with numeric probabilities for calibration tracking via Brier scoring. Predictions are scored against actual outcomes.

Instructions

Log a forward-looking assessment to the judgment ledger. Use this whenever making a prediction, recommendation, or forward-looking call. Prefer a numeric 'probability' (0-1) — it enables real calibration (Brier score) over time. Assessments are tracked and later scored against what actually happened.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe assessment or prediction, stated clearly
logicNoThe reasoning behind this assessment at the time
domainYesAssessment domain (one of the configured LEDGER_DOMAINS)
horizonNoExpected resolution timeframe e.g. '2 weeks', '3 months'
conditionsNoRelevant conditions or context at time of assessment
confidenceNoCategorical confidence, used only if 'probability' is omitted (high=0.9, moderate=0.75, low=0.6)moderate
probabilityNoForecast probability the call proves right, 0-1 (e.g. 0.7). Preferred over 'confidence'; enables Brier scoring.
source_sessionNoBrief label for where this came from e.g. 'planning session 2025-01-15'
Behavior4/5

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

No annotations, so description carries full burden. It discloses that assessments are tracked and scored later via Brier score, and explains the mapping for confidence to probability. No contradictions.

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?

Three concise sentences front-loading purpose, then usage guidance, then parameter advice. No extraneous 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?

Covers core purpose, parameter preferences, and tracking behavior. Lacks return value details (no output schema) but adequate for the tool's complexity.

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%, baseline 3. Description adds value by explaining why probability is preferred (Brier scoring) and how confidence defaults map to probabilities, going beyond schema doc.

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 verb 'log' and the resource 'judgment ledger'. It specifies forward-looking assessments and distinguishes from siblings like 'save_memory' by focusing on predictions. The explicit usage guidance reinforces 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?

Explicitly says 'use this whenever making a prediction, recommendation, or forward-looking call' and advises preferring probability over confidence. No explicit alternatives or exclusions, but clear context provided.

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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