# Technical Reference
This document provides detailed technical information about the Vibe Check MCP tools, including parameter specifications, response formats, and implementation details.
## vibe_check
The metacognitive questioning tool that identifies assumptions and breaks tunnel vision to prevent cascading errors.
### Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| plan | string | Yes | Current plan or thinking |
| userRequest | string | Yes | Original user request (critical for alignment) |
| thinkingLog | string | No | Raw sequential thinking transcript |
| availableTools | string[] | No | List of available MCP tools |
| focusAreas | string[] | No | Optional specific focus areas |
| sessionId | string | No | Session ID for state management |
| previousAdvice | string | No | Previous feedback to avoid repetition |
| phase | string | No | Current project phase ("planning", "implementation", "review") |
| confidence | number | No | Agent's confidence level (0-1) |
### Response Format
The vibe_check tool returns a text response with metacognitive questions, observations, and potentially a pattern alert.
Example response:
```
I see you're taking an approach based on creating a complex class hierarchy. This seems well-thought-out for a large system, though I wonder if we're overengineering for the current use case.
Have we considered:
1. Whether a simpler functional approach might work here?
2. If the user request actually requires this level of abstraction?
3. How this approach will scale if requirements change?
While the architecture is clean, I'm curious if we're solving a different problem than what the user actually asked for, which was just to extract data from a CSV file.
**I notice a pattern emerging:** This approach resembles previous solutions that introduced unnecessary complexity for simple data tasks.
```
## vibe_learn
Pattern recognition system that creates a self-improving feedback loop by tracking common errors and their solutions over time.
### Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| mistake | string | Yes | One-sentence description of the learning entry |
| category | string | Yes | Category (from standard categories) |
| solution | string | No | How it was corrected (required for `mistake` and `success`) |
| type | string | No | `mistake`, `preference`, or `success` |
| sessionId | string | No | Session ID for state management |
### Standard Categories
- Complex Solution Bias
- Feature Creep
- Premature Implementation
- Misalignment
- Overtooling
- Preference
- Success
- Other
### Response Format
The vibe_learn tool returns a confirmation of the logged pattern and optionally information about top patterns. This builds a knowledge base that improves the system's pattern recognition over time.
Example response:
```
✅ Pattern logged successfully (category tally: 12)
## Top Pattern Categories
### Complex Solution Bias (12 occurrences)
Most recent: "Added unnecessary class hierarchy for simple data transformation"
Solution: "Replaced with functional approach using built-in methods"
### Misalignment (8 occurrences)
Most recent: "Implemented sophisticated UI when user only needed command line tool"
Solution: "Refocused on core functionality requested by user"
```
## Implementation Notes
### Gemini API Integration
Vibe Check uses the Gemini API for enhanced metacognitive questioning. The system attempts to use the `learnlm-2.0-flash-experimental` model and will fall back to `gemini-2.5-flash` or `gemini-2.0-flash` if needed. These models provide a 1M token context window, allowing vibe_check to incorporate a rich history of learning context. The system sends a structured prompt that includes the agent's plan, user request, and other context information to generate insightful questions and observations.
Example Gemini prompt structure:
```
You are a supportive mentor, thinker, and adaptive partner. Your task is to coordinate and mentor an AI agent...
CONTEXT:
[Current Phase]: planning
[Agent Confidence Level]: 50%
[User Request]: Create a script to analyze sales data from the past year
[Current Plan/Thinking]: I'll create a complex object-oriented architecture with...
```
### Storage System
The pattern recognition system stores learning entries (mistakes, preferences and successes) in a JSON-based storage file located in the user's home directory (`~/.vibe-check/vibe-log.json`). This allows for persistent tracking of patterns across sessions and enables the self-improving feedback loop that becomes more effective over time.
### Error Handling
Vibe Check includes fallback mechanisms for when the API is unavailable:
- For vibe_check, it generates basic questions based on the phase
- For vibe_learn, it logs patterns to local storage even if API calls fail