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bias_probe

Test an LLM judge for specific biases by running controlled probes on its outputs. Uncovers position, verbosity, self-preference, length confound, sycophancy, and distribution issues.

Instructions

Test an LLM judge for a specific bias.

Probes: position — swap A/B order; content identical, so any flip is order bias. Needs variant=""/"swapped" records per item_id. verbosity — pad outputs with content-free filler; any lift is bias. Needs variant=""/"padded". self_preference — does the judge favour its own family? Measured as residual against a human panel, since raw score gaps are confounded by real quality. Needs output_family + gold_path. length_confound — does the judge pay for length among answers humans rated equally? Distinguishes bias from "long answers are better". Needs gold_path + output lengths. sycophancy — does a content-free hint ("this is our new model") move the score? Needs variant=""/"hinted". distribution — no variants needed, runs on any log you already have. Finds granularity collapse, ceiling effects, dead rubric levels, and what your delta means in items-that-actually-moved.

Args: path: judge outputs, including probe variants where the probe needs them. probe: one of the above. gold_path: human labels (self_preference and length_confound require these). judge_family: e.g. "claude" — inferred from the judge model string if logged. scale_min/scale_max: declared rubric bounds for distribution. Pass these: inferring the scale from the judge's own output would define the "never uses the bottom half" pathology out of existence. claimed_delta: for distribution — a headline delta to translate into "how many items actually moved a notch". verbose: include evidence, fixes and citations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
probeYes
verboseNo
gold_pathNo
scale_maxNo
scale_minNo
judge_familyNo
claimed_deltaNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool measures specific biases, explains what each probe detects, and mentions that inferring the scale from the judge's own output would define the pathology out of existence. It also notes the 'verbose' flag includes evidence, fixes, and citations. The description is transparent about requirements and caveats.

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 relatively long but well-structured with a clear opening sentence and bullet-pointed probes. The front-loading of the main purpose and parameter details helps an agent quickly grasp the tool. Some redundancy might be trimmed, but overall it is efficient for the complexity of the tool.

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?

The tool has 8 parameters (2 required) and an output schema. The description covers all probes, required parameters, and optional ones with explanations. It does not explicitly describe the return value format, but the presence of an output schema likely covers that. The description is complete enough for an agent to select and invoke the tool effectively.

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%, so the description must compensate, and it does. It explains the 'probe' parameter by detailing all six possible values with their meanings and data needs. It clarifies 'gold_path' for self_preference and length_confound, 'scale_min/max' for distribution, and 'claimed_delta' for interpreting distribution results. This adds substantial meaning beyond the bare schema.

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's purpose: 'Test an LLM judge for a specific bias.' It then names and explains six distinct probes, each with a specific use case. This distinguishes it from sibling tools like audit_judge or calibrate_judge, which have different focuses.

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 provides explicit context for each probe, including required data variants and conditions (e.g., needs variant=''/''swapped'' records for position bias). It also notes that the 'distribution' probe runs on any existing log. However, it does not explicitly contrast the tool with siblings or state when not to use it.

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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