docs/QUICK_START.md

Quick Verification Guide – Sentiment Evolution Tracker

Use this guide to validate the project in under five minutes before recording or presenting.

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⚡ Fast Track Checklist (≈5 minutes)

1. Environment (1 minute)

python --version            # confirm Python 3.10+

2. NLP Assets (2 minutes)

python -m textblob.download_corpora
python -m nltk.downloader punkt averaged_perceptron_tagger

3. Claude Desktop Wiring (2 minutes)

1. Open %APPDATA%\Claude\claude_desktop_config.json
2. Point the MCP entry to src/mcp_server.py
3. Save, close Claude completely, and relaunch (wait 30–40 seconds)

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✅ Claude Smoke Tests

Run these prompts in Claude Desktop (server running via python src\mcp_server.py).

Test 1 – Baseline Analysis (~30 s)

Analyze these customer messages:
- "I love your product"
- "but the price is too high"
- "I'm looking at alternatives"

Use analyze_sentiment_evolution, detect_risk_signals, and predict_next_action.

Expected: DECLINING sentiment, MEDIUM risk, MONITOR_CLOSELY recommendation.

Test 2 – Portfolio KPIs (~30 s)

Use get_database_statistics to tell me how many customers I have, how many are at risk, and the average sentiment.

Expected: 5 customers, 1 high-risk customer, average sentiment ≈ 68.

Test 3 – Customer History (~30 s)

Use get_customer_history with customer_id "ACME_CORP_001" and show the full history.

Expected: Detailed profile, multiple analyses, active alerts.

Test 4 – High-Risk Filter (~30 s)

Use get_high_risk_customers with threshold 0.5 and list the clients.

Expected: ACME_CORP_001 flagged at 85% risk.

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📊 Technical Verification

Confirm the MCP Server Is Alive

Get-Process | Where-Object {$_.Name -like "*python*"} | Format-Table ProcessName, Id

You should see the Python process running the MCP server.

Inspect the Database

python - <<'PY'
import sqlite3
conn = sqlite3.connect('data/sentiment_analysis.db')
cur = conn.cursor()
cur.execute('SELECT COUNT(*) FROM conversations')
print('Conversations:', cur.fetchone()[0])
conn.close()
PY

Expect a non-zero conversation count after loading demo data.

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🎯 Acceptance Criteria

• Functionality – All seven MCP tools execute without errors and persist data.
• Claude Integration – MCP server appears in Claude, and tool calls return coherent answers.
• Value Demonstrated – Historical analytics, alerts, and actions are visible.
• Code Quality – Modular structure, error handling, and documentation present.

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🚨 Troubleshooting

• Claude cannot see the server → verify the path in claude_desktop_config.json, restart Claude.
• Tool invocation fails → ensure dependencies are installed with Python 3.10+.
• Empty database → rerun python init_db.py and python tools\populate_demo_data.py.
• Import errors → run commands from the mcp-nlp-server folder.

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📁 Relevant Files

mcp-nlp-server/
├── README.md                  # full technical reference
├── docs/ARCHITECTURE.md       # architecture diagram and flow
├── docs/EXECUTIVE_SUMMARY.md  # stakeholder briefing
├── requirements.txt           # dependencies
├── data/sentiment_analysis.db # generated database
└── src/                       # MCP server and analysis modules

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💡 What Makes This Different

• Maintains persistent customer histories for Claude.
• Enables queries across the entire portfolio, not just the current chat.
• Demonstrates how MCP tooling unlocks agentic workflows with saved state.

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📞 Technical Snapshot

Item	Detail
Language	Python 3.10+
MCP SDK	0.1.x
Database	SQLite 3
MCP Tools	7
Response Time	< 100 ms per tool call on demo data

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For deeper documentation see README.md and the architecture notes in docs/.

4. Código ✅