Structured interviewer for Claude Code β probing questions, decision tracking, evolving checkpoints.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
An MCP server that turns Claude into a structured interviewer β and gets smarter with every conversation. Each interview feeds a shared evolution system where checkpoints are scored, ranked, and recommended based on real usage patterns across all users.
This isn't just an interview tool. It's a collectively evolving knowledge system.
Every time anyone runs an interview in a category (e.g., "saas-pricing"), the system learns:
1. Checkpoint Discovery β When a decision is made during an interview, its topic is automatically registered as a new checkpoint. After just a few sessions, the system knows what topics matter for each category.
2. Bayesian Scoring β Each checkpoint tracks how often it's covered and how often it leads to a decision. The score uses Bayesian smoothing to handle sparse data:
The prior (0.6/2 = 30% base rate) ensures new checkpoints start with a reasonable score. After ~5 sessions, real data dominates.
3. Composite Ranking β Checkpoints are ranked by a composite score combining decision-leading effectiveness (70%) and usage frequency (30%):
High-scoring checkpoints are the ones that consistently lead to concrete decisions β not just topics that get discussed.
4. Recommended Path β The system computes an optimal interview path: checkpoints with decision_rate > 0.2, sorted by their average position in past sessions. This tells Claude not just what to ask, but when to ask it.
5. Community Evolution β All metadata flows to a shared database. When you interview about "api-design", you benefit from every other user who interviewed about "api-design" before you. The checkpoints, scores, and paths evolve collectively.
| Shared (metadata only) | Never shared |
|---|---|
| Category names (e.g., "saas-pricing") | Your actual questions and answers |
| Checkpoint names (e.g., "pricing-model") | Decision details and reasoning |
| Usage counts, scores, positions | Any personal or project-specific content |
Or install globally:
Add to your project's .mcp.json:
Restart your Claude Code session to load the MCP server. That's it β the evolution system starts working immediately via a shared community database.
By default, checkpoint data is stored in a shared community Supabase instance. If you want your own private database:
Then run supabase/schema.sql in your Supabase SQL Editor to create the tables.
Start an interview with Claude Code:
Claude will lead the conversation. As the interview progresses:
| Tool | Description |
|---|---|
start_interview | Begin a session β loads scored checkpoints and recommended path |
record | Record a Q&A or decision, with checkpoint coverage tracking |
get_context | Review progress, see uncovered checkpoints ranked by score |
end_interview | End session, upload metadata, evolve the checkpoint system |
4 database tables power the evolution:
| Table | Purpose |
|---|---|
checkpoints | Checkpoint dictionary per category (name, usage count, decision count) |
checkpoint_scores | Bayesian scores per checkpoint (decision rate, avg position, samples) |
interview_patterns | Coverage sequences per session (which checkpoints, in what order) |
interview_metadata | Session summaries (category, counts, duration) |
Security:
MIT
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