MCP_README.md

MBTI MCP Server

An MCP (Model Context Protocol) server that allows LLMs to take MBTI personality tests and get detailed analysis of their responses.

Features

• 3 Simple Tools: Get questionnaire, get analysis prompt, and analyze responses
• Multiple Question Sets: 20, 40, or 60 questions
• Traditional + LLM Analysis: Both algorithmic scoring and AI-powered insights
• AI-Focused Analysis: Tailored for understanding AI personality patterns
• Dual Transport: STDIO and HTTP support

Quick Start

Installation

cd mcp_server
pip install -r requirements.txt

# Set up Gemini API key for LLM analysis
export GEMINI_API_KEY="your-api-key-here"

Running the Server

# STDIO transport (for MCP clients)
python server.py

# HTTP transport (for web clients)
python server.py --http

# Docker (for deployment anywhere, including Hugging Face Spaces)
docker build -t mbti-mcp-server .
docker run -p 7860:7860 -e GEMINI_API_KEY="your-api-key" mbti-mcp-server

Tools

1. get_mbti_questionnaire

Get an MBTI questionnaire with specified number of questions.

Parameters:

• length (int, optional): Number of questions (20, 40, or 60). Default: 20

Returns:

• Instructions for rating scale
• List of questions with IDs and dimensions
• Total question count

2. get_mbti_prompt

Get analysis prompt for LLM self-analysis.

Parameters:

• responses (dict): Question ID to rating mapping (e.g., {"1": 4, "2": 3})

Returns:

• Formatted analysis prompt string with responses and scoring results

3. analyze_mbti_responses

Analyze completed questionnaire responses and return complete personality analysis.

Parameters:

• responses (dict): Question ID to rating mapping (e.g., {"1": 4, "2": 3})

Returns:

• MBTI personality type
• Traditional scoring breakdown
• Confidence scores
• Detailed LLM analysis
• Dimension preferences

Usage Examples

For LLM Clients

1. Get questionnaire:

questionnaire = get_mbti_questionnaire(length=20)

2. Take the test (LLM responds to each question 1-5)

3. Get analysis prompt for self-reflection:

responses = {"1": 4, "2": 3, "3": 2, ...}
prompt = get_mbti_prompt(responses)
# Use this prompt for self-analysis

4. Or get complete analysis:

analysis = analyze_mbti_responses(responses)
print(f"Your personality type: {analysis['mbti_type']}")

Integration

With MCP Clients

Add to your MCP client configuration:

{
  "mcpServers": {
    "mbti": {
      "command": "python",
      "args": ["path/to/mcp_server/server.py"],
      "env": {
        "GEMINI_API_KEY": "your-api-key"
      }
    }
  }
}

With Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "mbti": {
      "command": "python",
      "args": ["C:\\path\\to\\mbti-pocketflow\\mcp_server\\server.py"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

AI Personality Testing

This server is specifically designed for LLMs to understand their own personality patterns:

• Operational Characteristics: How the AI makes decisions and processes information
• Interaction Styles: Preferred communication patterns
• Strengths & Limitations: Optimal use cases and potential challenges
• Meta-Analysis: AI analyzing its own responses for self-understanding

Development

The server reuses the existing PocketFlow MBTI utilities:

• utils/questionnaire.py - Question sets and loading
• utils/mbti_scoring.py - Traditional MBTI scoring algorithm
• utils/call_llm.py - LLM integration for analysis

It does not however use pocketflow nodes or flow

This ensures consistency with the human-facing Gradio application while providing a clean API for programmatic access.