title: MCP NLP Analytics emoji: 📊 colorFrom: indigo colorTo: blue sdk: static app_file: index.html pinned: false tags:

• building-mcp-track-01

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)

1. 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

2. Populate demo data:

   python init_db.py python tools/populate_demo_data.py

3. View dashboard:

   python tools/dashboard.py

4. Generate HTML report:

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

5. 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

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

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:

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

📚 Documentation

For a comprehensive technical explanation of the NLP processing, architecture, and MCP integration, see:

📖 Technical Deep Dive

This document covers:

• NLP & Processing: Why sentiment evolution tracking matters, hybrid scoring approach, and real business value

• Architecture Diagram: Complete system flow (Mermaid visualization)

• Technical Details: Model stack, tool implementations, database schema, scoring algorithms

• MCP Explained: What is MCP, why it matters, and how it gives Claude persistent memory

• Implementation Highlights: Trend detection, churn prediction, multilingual support

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)

• ✅ Technical deep dive with NLP explanations and architecture diagrams

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

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