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careerproof-mcp

careerproof-mcp

by detonnate
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Score a practice interview answer

careerproof_score_interview_answer

Scores a practice interview answer with rule-based checks for STAR structure, action verbs, measurable results, length, and competency keywords, returning actionable feedback.

Instructions

Rule-based (non-LLM) check of a candidate's practice answer: STAR structure presence, first-person action verbs, measurable results, length vs. a target word count, and keyword relevance to the target competency. Returns a score and specific, actionable feedback rather than a black-box grade.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "answerText": "<answerText>"
    },
    "name": "careerproof_score_interview_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
answerTextYesThe candidate's practice answer
competencyNoThe competency the answer is meant to address
questionIdNoInterview question ID to attach this score to
maximumWordsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses that the check is rule-based/non-LLM, lists what is evaluated, and promises actionable feedback rather than a black-box grade. It does not clarify whether the optional questionId causes the score to be persisted or attached, leaving a side-effect gap.

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 two sentences with no wasted words. It front-loads the core check and follows with the return behavior, making it easy for an agent to parse quickly.

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?

Given four parameters, no output schema, and no annotations, the description provides enough context for correct invocation: it explains what is checked and the nature of the returned score and feedback. It stops short of detailing score range, feedback format, or whether the score is persisted when questionId is supplied.

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

Parameters4/5

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

Schema coverage is 75%, and the description adds meaning beyond the schema by clarifying that maximumWords functions as a target word count and that competency drives keyword relevance. The answerText parameter is also directly described as the candidate's practice answer. Only questionId is left to the schema alone.

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?

The description states a specific verb and resource: it checks a candidate's practice answer, enumerating five concrete dimensions (STAR structure, action verbs, measurable results, length, keyword relevance). It is clearly distinguishable from sibling generation tools like generate_star_answer, but it does not explicitly name or differentiate them.

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 the tool is for evaluating practice answers, but it gives no explicit when-to-use guidance, prerequisites, or alternatives (e.g., when to score versus when to generate a STAR answer). Usage is inferable from the title and first sentence, which meets the minimum viable threshold.

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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