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get_interview_prep_prompt

Prepare for medical writing interviews by generating role-specific questions and STAR-based answer frameworks. Get 10 likely questions, structured answers, and 5 smart questions to ask.

Instructions

[PRO] Prepare for a medical writing interview with role-specific questions and STAR frameworks. Generates 10 likely questions, answer frameworks, and 5 smart questions to ask.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_titleYes
company_typeYes
key_responsibilitiesYes
your_key_experiencesYes
focus_areaYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The actual handler function for the 'get_interview_prep_prompt' tool. It is decorated with @mcp.tool() and takes 5 parameters (job_title, company_type, key_responsibilities, your_key_experiences, focus_area). It returns a formatted prompt string asking an LLM to generate 10 interview questions with STAR-format frameworks and 5 questions to ask the interviewer.
    @mcp.tool()
    def get_interview_prep_prompt(
        job_title: str,
        company_type: str,
        key_responsibilities: str,
        your_key_experiences: str,
        focus_area: str
    ) -> str:
        """
        [PRO] Prepare for a medical writing interview with role-specific questions and STAR frameworks.
        Generates 10 likely questions, answer frameworks, and 5 smart questions to ask.
        """
        return f"""I have an interview for {job_title} at {company_type}.
    The role focuses on {key_responsibilities}.
    
    Generate:
    1. 10 likely interview questions specific to this role
    2. For each question, a STAR-format answer framework using: {your_key_experiences}
    3. 5 smart questions I should ask the interviewer
    
    Focus especially on: {focus_area}"""
  • server.py:1003-1003 (registration)
    Registration entry listing the tool as 'get_interview_prep_prompt' in the PRO tier tool directory with description 'Prepare for medical writing interview'.
    ("get_interview_prep_prompt", "Prepare for medical writing interview"),
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the generative behavior (produces questions and frameworks) and quantifies outputs (10 and 5). However, it omits details like whether it is read-only, destructive, requires authentication, or has rate limits. The description adds value but lacks depth.

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 extremely concise: two sentences with no filler. It front-loads the purpose and immediately specifies outputs. Every word earns its place, making it efficient for an AI agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema (which explains return values), the description lacks guidance on how to provide input parameters. With five required parameters and no explanation, the agent may not know what values to supply. The description is incomplete for a tool of this complexity.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain any of the five parameters (job_title, company_type, key_responsibilities, your_key_experiences, focus_area). The description only mentions 'role-specific' output but does not map parameters to their purpose or usage. With 0% coverage, the description fails to compensate.

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: preparing for a medical writing interview by generating role-specific questions and STAR frameworks. It specifies the exact outputs: 10 likely questions, answer frameworks, and 5 smart questions. This distinguishes it from sibling tools that focus on other medical writing tasks.

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 clear context for when to use the tool: when preparing for a medical writing interview. It implies the usage scenario but does not explicitly state when not to use it or list alternatives. However, the sibling tools cover different domains, making the intended use clear.

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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