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interview-prep-mcp

interview-prep-mcp

by shenmali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_questions_tool

Generate tailored interview questions based on job, CV, and research context. Specify count and question types like technical or behavioral.

Instructions

Generate count interview questions using job + cv + research as context.

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": {
      "interview_id": "<interview_id>",
      "count": "<count>",
      "types": "<types>"
    },
    "name": "generate_questions_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
interview_idYes
countYes
typesYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It mentions using job, CV, and research as context but does not explain how these are used, whether the tool is read-only or generates new data, or the nature of the output (e.g., whether it modifies state). The term 'generate' implies creation, but details are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words, but the phrasing 'Generate count interview questions' is ambiguous and slightly ungrammatical. It could be restructured to clearly indicate generating a specified number of questions. It is concise but at the cost of clarity.

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?

Given the tool has 3 required parameters, 0% schema coverage, and an output schema (not detailed), the description does not adequately cover input requirements, output format, or prerequisites (e.g., needing an active interview prep). For a generative tool with multiple inputs, this is insufficient for reliable agent usage.

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

Parameters2/5

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

Schema coverage is 0%, so parameters have no descriptions. The description only mentions 'count' indirectly ('Generate count'), but does not explain interview_id or types. The types enum is not elaborated. The reference to 'job + cv + research' hints at interview_id's role but is insufficient for precise parameter understanding.

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 clearly states the tool generates interview questions using job, CV, and research context. It distinguishes from sibling tools like list_questions_tool (which lists existing questions) and start_interview_prep_tool (which starts preparation). However, the phrasing 'Generate count interview questions' is slightly awkward and could be more precise.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or context-specific usage. For an agent to decide between generate_questions_tool and list_questions_tool or submit_practice_answer_tool, additional guidance is needed.

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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