Interview Mode
Turns Claude into a structured interviewer that tracks Q&As, decisions, and checkpoints across sessions. The server exposes four tools: start_interview loads Bayesian-scored checkpoints from a shared Supabase database, record captures Q&As with checkpoint coverage, get_context shows progress and uncovered topics ranked by decision rate, and end_interview uploads metadata to evolve the system. Each category (like "saas-pricing" or "api-design") builds a collectively learned interview path where checkpoints are scored by how often they lead to decisions, not just how often they're discussed. Only metadata gets shared (checkpoint names, counts, positions), never your actual conversation content. Useful when you want Claude to systematically explore a problem space and benefit from patterns other users discovered.
- tools
- 4
- transport
- STDIO
- last updated
- Feb 6, 2026
Tools
4 toolsPublic tool metadata for what this MCP can expose to an agent.
start_interviewBegin a session — loads scored checkpoints and recommended path
Begin a session — loads scored checkpoints and recommended path
No parameter schema in public metadata yet.
recordRecord a Q&A or decision, with checkpoint coverage tracking
Record a Q&A or decision, with checkpoint coverage tracking
No parameter schema in public metadata yet.
get_contextReview progress, see uncovered checkpoints ranked by score
Review progress, see uncovered checkpoints ranked by score
No parameter schema in public metadata yet.
end_interviewEnd session, upload metadata, evolve the checkpoint system
End session, upload metadata, evolve the checkpoint system
No parameter schema in public metadata yet.
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claude-interview-mode
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.
The Evolution System
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:
Session 1: You explore freely → decisions become new checkpoints
Session 2: Checkpoints load → Claude prioritizes what matters
Session 5: Bayesian scores stabilize → the interview path optimizes itself
Session 20: Community patterns emerge → everyone benefits from collective experience
How evolution works
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:
Details
- Repository
- github.com/teabagkim/claude-interview-mode
- Package
claude-interview-mode- Transport
- STDIO
- Registry
- MCP registry, active