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ct-mcp

by justguy

score_response_quality

Evaluate response quality across substance, specificity, hedge avoidance, and structure. Discover the weakest dimension and receive targeted advice to improve it.

Instructions

Score a response across four quality dimensions: substance, specificity, hedge avoidance, and structure.

REQUIRED INPUT FORMAT — copy this structure exactly: {"response_text":"The full text of the response you want to evaluate for quality. It should be at least 10 characters.","claims":["Optional array of explicit claims"],"evidence":["Optional array of evidence items"]}

Dimensions:

  • substance_score: Shannon entropy on word frequencies (lexical diversity)

  • specificity_score: Density of concrete, quantitative markers

  • hedge_density: Proportion of hedging language (lower is better)

  • structure_score: Presence of claim->evidence->conclusion pattern

  • overall_score: Weighted average (substance 0.3, specificity 0.3, 1-hedge 0.2, structure 0.2)

Returns the weakest dimension with targeted improvement advice.

Optionally pass "context" with prior iteration data for escalation and stall detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimsNoOptional explicit claims to check for
contextNoOptional caller-provided context for iterative enforcement. Include prior failure counts, iteration history, and previous response data to enable escalation and stall detection. Omit for one-shot usage.
evidenceNoOptional evidence items to check for
response_textYesThe response text to evaluate (min 10 characters)
Behavior5/5

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

With no annotations provided, the description carries full behavioral disclosure. It thoroughly explains the scoring dimensions, the weighting formula, and specifically states that it 'Returns the weakest dimension with targeted improvement advice.' It also discloses the optional use of context for escalation and stall detection, leaving no ambiguity about tool behavior.

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 well-structured and front-loaded with the core purpose. The required input format is presented in a clear code block, followed by concise bullet-like explanations of dimensions and weighting, and a closing note on optional context. Every section earns its place without redundancy.

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?

No output schema exists, so the description appropriately explains the return value ('Returns the weakest dimension with targeted improvement advice'). It also covers all necessary behavior, including the scoring method and optional iterative context, making the tool fully understandable for an agent.

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?

Even though schema coverage is 100%, the description adds considerable meaning beyond the schema. It provides the exact required input format as a copyable JSON structure, clarifies that 'claims' and 'evidence' are optional, and explains how the 'context' parameter enables escalation and stall detection. This goes far beyond the schema's property descriptions.

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 opens with a specific verb and resource: 'Score a response across four quality dimensions: substance, specificity, hedge avoidance, and structure.' This clearly differentiates it from sibling tools that validate confidence, reasoning chains, or numeric claims by focusing on overall response quality across defined dimensions.

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

Usage Guidelines3/5

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

The description implies usage by defining what the tool scores and mentions optional context for iterative enforcement, but it does not explicitly state when to use this tool versus alternative validation tools, nor does it provide when-not-to-use guidance. The context paragraph hints at iterative workflows but lacks direct alternatives or exclusions.

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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