The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Interview Mode listing page.
An MCP server that turns Claude into a structured interviewer — 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 — 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 — 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 — 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 — not just topics that get discussed.
4. Recommended Path — 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 — 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 — 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 — 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