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HoziMurayama

MCP Rubber Duck

Official
by HoziMurayama

Duck Judge

duck_judge
Read-only

Compare and rank multiple AI responses using a customizable judge persona and criteria to select the best answer.

Instructions

Have one duck evaluate and rank other ducks' responses. Use after duck_council to get a comparative evaluation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
judgeNoProvider name of the judge duck (optional, uses first available)
personaNoJudge persona (e.g., "senior engineer", "security expert")
criteriaNoEvaluation criteria (default: ["accuracy", "completeness", "clarity"])
responsesYesArray of duck responses to evaluate (from duck_council output)
Behavior4/5

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

The annotations already declare readOnlyHint and openWorldHint, and the description adds the core comparative ranking behavior. It does not contradict the annotations. However, it doesn't elaborate on how the ranking is returned or whether it involves external model calls beyond the schema's judge enum, leaving some behavioral detail implicit.

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?

The description is exactly two sentences, front-loaded with the key verb ('evaluate and rank') and includes a usage instruction. No filler or repetition; every word earns its place.

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 description is adequate given the comprehensive schema and annotations: it states the action, the workflow position, and the purpose. The only gap is the lack of detail on the output format of the ranking (since no output schema exists), but the word 'rank' implies a comparative result, which is likely sufficient for an agent.

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 coverage is 100% with clear descriptions for all four parameters (judge, persona, criteria, responses). The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 a specific action ('evaluate and rank') on a specific resource ('other ducks' responses'). The phrase 'after duck_council' anchors its role relative to sibling tools like duck_council, compare_ducks, and duck_vote, making it unambiguous.

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

Usage Guidelines5/5

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

It gives explicit guidance: 'Use after duck_council to get a comparative evaluation.' This tells the agent when to use it (following duck_council) and the purpose, distinguishing it from alternatives like compare_ducks or duck_vote that may serve different comparison workflows.

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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