Interview Prep MCP Agent
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_MODEL | No | The OpenAI model to use | gpt-5.4-mini |
| OPENAI_API_KEY | Yes | Your OpenAI API key |
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_resumeA | Read the candidate's uploaded resume. Call before personalizing advice. |
| get_job_descriptionA | Read the target job description. Call before analyzing fit or generating questions. |
| analyze_skill_gapsB | Compare the uploaded resume and job description and return evidence-based skill coverage. |
| generate_interview_questionsB | Generate 1–10 personalized technical, behavioral, and design interview questions. |
| evaluate_answerB | Score an interview answer against relevance, STAR structure, specificity, and impact. |
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 clearly distinct purpose: data retrieval (get_resume, get_job_description), comparison (analyze_skill_gaps), question generation (generate_interview_questions), and answer evaluation (evaluate_answer). There is no overlap between tool responsibilities.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., analyze_skill_gaps, get_resume). No mixing of conventions or vague verbs.
With 5 tools covering data ingestion, analysis, generation, and evaluation, the count is well-scoped for an interview preparation assistant. Each tool earns its place without redundancy.
The tool set covers the core workflow: retrieve inputs, analyze gaps, generate questions, and evaluate answers. A minor gap is that there is no tool to generate specific improvement suggestions based on answer scores, but the existing evaluate_answer provides scoring that indirectly supports feedback.