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Run blind A/B comparisons between two outputs using multiple AI judges to determine preference without position bias. Get objective scores for translations, code, or summaries.

Instructions

Blind A/B comparison of two items judged by N models.

When blind=True (default), randomizes which item is shown as A vs B to each judge independently, then de-randomizes scores. This prevents position bias where judges consistently favor the first item shown.

Use for comparing translations, code solutions, summaries, or any pair of outputs where you want an objective preference.

Args: item_a: First item to compare item_b: Second item to compare context: Optional context for the comparison (e.g. the original task) dimensions: Scoring dimensions (default ["quality"]) scale: Rating scale as "min-max" (default "1-5") judge_count: Number of judge models (default 3) blind: Randomize A/B order per judge to prevent position bias (default true) min_tier: Minimum quality tier for judge selection (default "A") free_only: If true, only use free models as judges max_tokens: Max response tokens per judge (default 512) temperature: Sampling temperature (default 0.0)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blindNo
scaleNo1-5
item_aYes
item_bYes
contextNo
min_tierNoA
free_onlyNo
dimensionsNo
max_tokensNo
judge_countNo
temperatureNo

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 carries the burden of transparency. It explains the blind randomization to prevent position bias, the role of multiple judges, and all parameter behaviors (defaults for judge_count, temperature, etc.). No contradictions or hidden 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?

The description is well-structured: a clear purpose sentence, mechanism explanation, usage examples, then a bullet-like list of args. It is slightly verbose but front-loaded with essential details, justifying its length.

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?

Given the tool's complexity (11 parameters, output schema exists), the description covers all parameters, behavioral nuances, and usage context. No gaps remain for typical use cases, making it complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds thorough meaning to all 11 parameters, including defaults and interpretations (e.g., 'blind: Randomize A/B order per judge to prevent position bias'). This goes far beyond the schema's minimal titles.

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 tool does 'Blind A/B comparison of two items judged by N models,' specifying the verb, resource, and the unique blind mechanism. It distinguishes well from sibling tools like 'ask', 'judge', and 'batch_judge' by focusing on comparative evaluation.

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?

The description explicitly states when to use the tool ('for comparing translations, code solutions, summaries, or any pair of outputs') and explains the blind setting. However, it does not mention when not to use it or provide alternatives among siblings, which would improve guidance.

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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