Placed MCP Server

get_interview_questions

Generate relevant interview questions from a job description and company name to prepare for technical and behavioral interviews.

Instructions

Generate likely interview questions based on a job description and company. Helps prepare for interviews with relevant technical and behavioral questions.

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": {
      "job_description": "<job_description>"
    },
    "name": "get_interview_questions"
  }
}

Input Schema

Name	Required	Description
`job_description`	Yes	Job description text
`company`	No	Company name
`question_count`	No	Number of questions to generate (default: 10)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0May 8, 2026

TDQS

A3.5/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It states 'generate' but does not clarify if the generation is stateless, requires authentication, or has side effects (e.g., saving questions). No mention of rate limits or output format, leaving the agent uncertain about the tool's 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?

Two concise sentences front-load the purpose and context. Every sentence adds value with no redundancy or fluff.

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

Completeness3/5

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

The description is adequate for a generation tool with three parameters, but it lacks details on output format (e.g., list of strings vs. structured categories) and ignores that 'company' is optional. Given no output schema, more context on return value would improve completeness.

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?

The input schema covers 100% of the parameters with descriptions (e.g., 'Number of questions to generate (default: 10)'), so the baseline is 3. The description adds no extra meaning beyond the schema; it does not specify required format for job_description or company.

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 it 'generates likely interview questions' based on job description and company, specifying 'technical and behavioral questions'. The verb 'generate' and the scope are specific, distinguishing it from the sibling 'get_behavioral_questions' which likely only returns behavioral questions.

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 for interview preparation ('Helps prepare for interviews'), but does not explicitly state when to use this tool versus alternatives like 'get_behavioral_questions' or 'research_company'. No when-not or contextual exclusions are provided.

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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