Legal Text Analysis Project Integration Guide

Updated Documentation with MCP Setup and Data Integration Steps

1. Project Overview

Our project combines three main components:

• Original data analysis (3 Jupyter notebooks)
• SQLite database for structured storage
• MCP integration for AI-assisted analysis

2. Successfully Completed Steps

Database Setup

-- Database locationD:\\Projects\\LegalTextAnalysis\\data\\legal_text.db
-- Verified tables structurelegal_cases
text_metrics
tf_idf_scores

MCP Configuration

We successfully configured MCP after troubleshooting:

{  "mcpServers": {    "sqlite": {      "command": "uvx",      "args": ["mcp-server-sqlite", "--db-path", "./data/legal_text.db"],      "cwd": "D:/Projects/LegalTextAnalysis"    }  }}

Verification Commands

# Verify database existenceTest-Path "D:\\Projects\\LegalTextAnalysis\\data\\legal_text.db"# Start MCP serveruvx mcp-server-sqlite --db-path "./data/legal_text.db"

3. Next Steps: Data Integration

3.1 Loading CSV Data

To import your legal_text_classification.csv into the database:

import pandas as pd
import sqlite3
def load_legal_data():
    # Read the CSV file    df = pd.read_csv('legal_text_classification.csv')
    # Connect to the database    conn = sqlite3.connect('D:/Projects/LegalTextAnalysis/data/legal_text.db')
    # Load into legal_cases table    df.to_sql('legal_cases', conn, if_exists='append', index=False)
    return "Data loaded successfully!"

3.2 Integrating Jupyter Analysis

Your Jupyter notebooks likely contain valuable analysis. We’ll:

1. Extract metrics from notebooks
2. Store results in text_metrics
3. Save TF-IDF scores in tf_idf_scores

3.3 Creating Python Integration Script

# analysis_integration.pyimport pandas as pd
import sqlite3
from sklearn.feature_extraction.text import TfidfVectorizer
class LegalTextIntegration:
    def __init__(self, db_path):
        self.db_path = db_path
    def connect_db(self):
        return sqlite3.connect(self.db_path)
    def import_csv(self, csv_path):
        df = pd.read_csv(csv_path)
        with self.connect_db() as conn:
            df.to_sql('legal_cases', conn,
                     if_exists='replace',
                     index=False)
    def update_metrics(self, metrics_df):
        with self.connect_db() as conn:
            metrics_df.to_sql('text_metrics', conn,
                            if_exists='append',
                            index=False)