Sentiment Evolution Tracker – MCP Monitoring Stack

Sentiment Evolution Tracker is an enterprise-ready monitoring stack that runs as a Model Context Protocol (MCP) server. It combines local sentiment analytics, churn prediction, alerting, and reporting, and can operate standalone or alongside Claude Desktop as an intelligent assistant.

Why This Exists

Traditional "use Claude once and move on" workflows do not keep historical context, trigger alerts, or generate portfolio-level insights. Sentiment Evolution Tracker solves that by providing:

Automated trend detection (RISING / DECLINING / STABLE)
Churn probability scoring with configurable thresholds
Persistent customer histories in SQLite
Real-time alerts when risk exceeds 70%
ASCII and HTML visualizations for demos and stakeholders
Seven MCP tools that Claude (or any MCP-compatible LLM) can invoke on demand

🎥 Demo Video

A demo video (3-4 minutes) showing live sentiment analysis, risk detection, and MCP integration with Claude:

Watch on YouTube

The video demonstrates:

Live sentiment analysis of customer conversations
Risk detection and churn prediction
MCP tool invocation via Claude Desktop
Real-time alerts and reporting

How to Use

Quick Start (5 minutes)

Clone and install:

git clone https://github.com/RubenReyesss/mcp-nlp-analytics.git
cd mcp-nlp-server
pip install -r requirements.txt
python -m textblob.download_corpora
python -m nltk.downloader punkt averaged_perceptron_tagger

Populate demo data:

python init_db.py
python tools/populate_demo_data.py

View dashboard:
```
python tools/dashboard.py
```

Generate HTML report:

python tools/generate_report.py
# Opens data/reporte_clientes.html in your browser

Integrate with Claude Desktop:
- Edit config/claude_desktop_config.json with your actual path
- Restart Claude Desktop
- Start the MCP server: python src/mcp_server.py
- Now Claude can access all 7 sentiment analysis tools

Installation

cd mcp-nlp-server
pip install -r requirements.txt
python -m textblob.download_corpora
python -m nltk.downloader punkt averaged_perceptron_tagger

Daily Operations

python init_db.py – rebuilds the database from scratch (reset option)
python tools\populate_demo_data.py – loads deterministic demo customers
python tools\dashboard.py – terminal dashboard (Ctrl+C to exit)
python tools\generate_report.py – creates data/reporte_clientes.html
python src\mcp_server.py – launch the MCP server for Claude Desktop

MCP Tool Suite

Tool	Purpose
`analyze_sentiment_evolution`	Calculates sentiment trajectory for a set of messages
`detect_risk_signals`	Flags phrases that correlate with churn or dissatisfaction
`predict_next_action`	Forecasts CHURN / ESCALATION / RESOLUTION outcomes
`get_customer_history`	Retrieves full timeline, sentiment, and alerts for a customer
`get_high_risk_customers`	Returns customers whose churn risk is above a threshold
`get_database_statistics`	Portfolio-level KPIs (customers, alerts, sentiment mean)
`save_analysis`	Persists a custom analysis entry with full metadata

Data Model (SQLite)

customer_profiles – customer metadata, lifetime sentiment, churn risk, timestamps
conversations – every analysis entry, trend, predicted action, confidence
risk_alerts – generated alerts with severity, notes, and resolution state

Database files live in data/sentiment_analysis.db; scripts automatically create the directory if needed.

Claude Desktop Integration

config/claude_desktop_config.json registers the server:

{
  "mcpServers": {
    "sentiment-tracker": {
      "command": "python",
      "args": ["src/mcp_server.py"],
      "cwd": "C:/Users/Ruben Reyes/Desktop/MCP_1stHF/mcp-nlp-server"
    }
  }
}

Restart Claude Desktop after editing the file. Once connected, the seven tools above appear automatically and can be invoked using natural language prompts.

Documentation Map

docs/QUICK_START.md – five-minute functional checklist
docs/ARCHITECTURE.md – diagrams, module responsibilities, data flow
docs/HOW_TO_SAVE_ANALYSIS.md – practical guide for the save_analysis tool
docs/EXECUTIVE_SUMMARY.md – executive briefing for stakeholders
docs/CHECKLIST_FINAL.md – submission readiness checklist

Tech Stack

Python 3.10+
MCP SDK 0.1+
SQLite (standard library)
TextBlob 0.17.x + NLTK 3.8.x
Chart.js for optional HTML visualizations

Status

✅ Production-style folder layout
✅ Deterministic demo dataset for the hackathon video
✅ Comprehensive English documentation
✅ Tests for the save_analysis workflow (tests/test_save_analysis.py)

Run python tools\dashboard.py or open the generated HTML report to verify data before your demo, then start the MCP server and launch Claude Desktop to show the agentic workflow in real time.

Team

Role	Contributor	GitHub
Developer	RubenReyesss	@RubenReyesss

Track

This project is submitted to Track 1: Building MCPs (building-mcp-track-01).

It demonstrates a production-ready MCP server that extends Claude's capabilities with persistent analytics, risk prediction, and alerting—solving the limitation that Claude lacks memory, database writes, and automated monitoring.

📱 Social Media Post

Announcement on LinkedIn:

Read the full post on LinkedIn

Featured in: MCP 1st Birthday Hackathon

Resources

GitHub Repository: https://github.com/RubenReyesss/mcp-nlp-analytics
Hugging Face Space: https://huggingface.co/spaces/MCP-1st-Birthday/mcp-nlp-analytics
Demo Video: https://youtu.be/h2tNu2KTPQk
LinkedIn Post: https://www.linkedin.com/posts/rubenreyesparra_mcp-nlp-analytics-a-hugging-face-space-activity-7400976539959390208-SG3Q

Made with ❤️ for the Anthropic MCP 1st Birthday Hackathon