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cuba_calibrar

Calibrates prediction confidence by tracking verify outcomes and computing per-source Bayesian credibility, Brier score, and expected calibration error.

Instructions

Bayesian confidence calibration: track verify predictions, mark outcomes, compute P(correct|level). Closes the feedback loop between faro verify and eco correct. v0.9: action 'trust' returns per-source credibility (Beta posterior updated by resolve outcomes; Yin-Han-Yu IEEE TKDE 2008).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results for history (default 20)
actionYesCalibration action. v0.9: 'trust' returns per-source Beta(α, β) credibility; 'metrics' returns Brier score (1950) + Expected Calibration Error (Naeini AAAI 2015) + reliability diagram.
outcomeNoWhether the verify prediction was right (for resolve)
verify_idNoVerify log UUID (for resolve)
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses that 'trust' returns credibility updated by resolve outcomes, but doesn't clarify if other actions modify data, auth requirements, or side effects.

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?

Two concise sentences with front-loaded purpose. However, jargon like 'P(correct|level)' and references reduce accessibility.

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?

No output schema; description lacks details on return formats for actions like 'stats', 'history', and 'resolve'. Incomplete for a multi-action calibration tool.

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 covers all parameters (100% coverage). Description adds minor context for 'trust' and 'metrics' actions with references, but doesn't significantly enhance understanding beyond schema.

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 performs Bayesian confidence calibration, tracking predictions and computing probabilities. It clearly identifies the resource and verb, but doesn't differentiate from sibling tools like cuba_faro or cuba_eco, which might have overlapping functions.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description mentions closing a feedback loop but doesn't specify context or exclusion criteria.

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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