Interview Prep MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FREE_PDF_URL_IB | No | Optional link shown as the "Free Top 30 questions PDF" footer for Investment Banking results. | |
| PLAYBOOK_URL_IB | No | Optional link shown as the "Full interview playbook" footer for Investment Banking results. | |
| FREE_PDF_URL_AIE | No | Optional link shown as the "Free Top 30 questions PDF" footer for AI Engineer results. | |
| PLAYBOOK_URL_AIE | No | Optional link shown as the "Full interview playbook" footer for AI Engineer results. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_topicsA | List the roles (ai-engineer, investment-banking) and topics in the question bank, with question counts per level (Entry/Mid/Senior). Call this first to get valid topic ids. |
| get_questionsA | Return interview questions for a role, optionally filtered by topic and level, with model answers if include_answers is true. Supports paging with offset. |
| get_answer_frameworkA | For one question id (e.g. aie-026, ib-003), return a step-by-step answer framework, the model answer, the likely follow-up and the common mistake, plus a 1-5 self-scoring rubric. |
| random_drillA | Pick n random questions (no answers) for a timed mock round. Optionally filter by topic and level. Pass seed to replay the same drill. Reveal answers with get_answer_framework. |
| search_questionsA | Keyword search across questions and model answers, e.g. 'KV cache', 'accretion dilution', 'prompt injection'. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a reasonably distinct role: list_topics is an index, get_questions is filtered retrieval, get_answer_framework is single-question deep dive, random_drill is a timed mock, and search_questions is keyword lookup. The only real overlap is between get_questions and search_questions (both return question lists) and between random_drill and get_questions (both sample questions), but the descriptions make the intent differences clear.
Four of five names follow a clean verb_noun pattern (list_topics, get_questions, get_answer_framework, search_questions). random_drill breaks the pattern as an adjective+noun with no verb, though it is still readable and unambiguous.
Five tools is well-scoped for a question-bank server: discover, retrieve, drill, deep-dive, and search. Each tool earns its place with no redundancy padding the surface.
The core read lifecycle is covered: topic discovery, filtered retrieval, answer frameworks with rubrics, random drilling, and search. Minor gaps exist — no single-question fetch by id independent of the answer framework, and no attempt/progress tracking across drill sessions — but agents can work around both.