chore: remove redundant documentation files

docs/IMPLEMENTACION_SAVE_ANALYSIS.md

@@ -1,27 +0,0 @@
-# Implementation Notes – `save_analysis` Tool
-
-This memo documents the addition of the `save_analysis` MCP tool, which allows Claude (or any MCP client) to persist custom sentiment reviews under a specific `customer_id`.
-
-## Key Changes
-
-- Updated `src/mcp_server.py` to register the `save_analysis` tool and validate payloads before writing to SQLite.
-- Added unit coverage in `tests/test_save_analysis.py` to confirm inserts, risk updates, and database integrity.
-- Expanded documentation in `docs/README.md` and `docs/HOW_TO_SAVE_ANALYSIS.md` with examples and verification steps.
-
-## Supporting Assets
-
-- `docs/HOW_TO_SAVE_ANALYSIS.md` – step-by-step operator guide and FAQ.
-- `tests/test_save_analysis.py` – regression suite executed during CI/local validation.
-- `tools/view_customer_profile.py` – CLI helper to verify saved analyses.
-
-## Usage Checklist
-
-1. Run `python tools/view_customer_profile.py <CUSTOMER_ID>` after the tool executes to confirm persistence.
-2. Regenerate portfolio metrics with `python tools/view_database.py` if you need to showcase updated KPIs.
-3. Include the `save_analysis` tool in demo scripts so reviewers can see the end-to-end workflow.
-
-## Status
-
-- All seven MCP tools (analysis, risk, reporting, and persistence) are available.
-- Demo dataset includes customers with stored `save_analysis` entries for immediate validation.
-- Documentation and tests are aligned with the released functionality.

docs/README.md

@@ -1,109 +0,0 @@
-# Sentiment Evolution Tracker – MCP Server Guide
-
-This reference explains how to set up, operate, and extend the Sentiment Evolution Tracker. The system integrates with Claude Desktop via the Model Context Protocol (MCP) and provides persistent sentiment analytics, churn prediction, and portfolio reporting.
-
-## Sections
-
-- Overview
-- Feature Highlights
-- Installation
-- Operating Checklist
-- MCP Tool Contracts
-- Data Model
-- Troubleshooting
-- Roadmap & Licensing
-
----
-
-## Overview
-
-Claude excels at single-session analysis but forgets historical context. Sentiment Evolution Tracker solves this gap with an MCP server that:
-
-- Stores customer interactions and analyses in SQLite.
-- Detects sentiment trends (rising, declining, stable).
-- Calculates churn probability and surfaces alerts above configurable thresholds.
-- Provides dashboards and MCP tools for portfolio-level insights.
-
-## Feature Highlights
-
-- Lightweight NLP pipeline (TextBlob + NLTK) for real-time scoring.
-- Risk signal detection (pricing pressure, competitor mentions, frustration).
-- Next-best-action recommendation (CHURN / ESCALATION / RESOLUTION / MONITOR_CLOSELY).
-- Seven MCP tools covering analytics and data retrieval.
-- Deterministic demo dataset for rehearsed demos or recordings.
-- CLI utilities: ASCII dashboard, HTML report generator, database viewers.
-
-## Installation
-
-```powershell
-cd mcp-nlp-server
-pip install -r requirements.txt
-python -m textblob.download_corpora
-python -m nltk.downloader punkt averaged_perceptron_tagger
-```
-
-Register the MCP server in `%APPDATA%\Claude\claude_desktop_config.json`:
-
-```json
-{
-  "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 saving the configuration.
-
-## Operating Checklist
-
-```powershell
-python init_db.py                     # reset database (optional)
-python tools\populate_demo_data.py   # load deterministic sample data
-python tools\dashboard.py            # check ASCII KPIs (Ctrl+C to exit)
-python tools\generate_report.py      # build data/reporte_clientes.html
-python src\mcp_server.py             # start MCP server for Claude
-```
-
-Once the server is running, Claude Desktop surfaces the tools automatically.
-
-## MCP Tool Contracts
-
-| Tool | Purpose |
-| --- | --- |
-| `analyze_sentiment_evolution` | Scores each message 0–100 and labels the overall trend. |
-| `detect_risk_signals` | Surfaces pricing pressure, competitor mentions, frustration phrases. |
-| `predict_next_action` | Suggests the most likely outcome (churn, escalation, resolution, monitor). |
-| `get_customer_history` | Returns profile data, interaction history, and open alerts. |
-| `get_high_risk_customers` | Lists customers whose churn risk exceeds a threshold. |
-| `get_database_statistics` | Provides portfolio KPIs (totals, active alerts, average sentiment). |
-| `save_analysis` | Persists user-specified analysis results with metadata for future queries. |
-
-## Data Model
-
-| Table | Description |
-| --- | --- |
-| `customer_profiles` | Aggregated metrics per customer, including lifetime sentiment and churn risk. |
-| `conversations` | Each stored analysis with timestamps, trends, predicted actions, confidence. |
-| `risk_alerts` | Alerts generated when risk or thresholds exceed configured limits. |
-
-## Troubleshooting
-
-- **Claude cannot find the MCP server** – Verify the absolute path in `claude_desktop_config.json` and restart Claude Desktop after editing.
-- **Import errors or missing modules** – Ensure the virtual environment uses Python 3.10+ and rerun `pip install -r requirements.txt`.
-- **No demo data appears** – Execute `python init_db.py` followed by `python tools\populate_demo_data.py`.
-- **Sentiment results feel off** – Provide at least three customer utterances and include relevant context or escalation history.
-
-## Roadmap & Licensing
-
-- Transformer-based sentiment and emotion tagging.
-- Realtime alert delivery via Slack or email.
-- Optional REST API for external integrations.
-- Expanded multilingual support and PDF/CSV export routines.
-
-The project is released under the MIT License (`LICENSE`).
-
-Maintainer: Rubén Reyes · November 2025 · Version 1.0.0

docs/README_MCP.md

@@ -1,161 +0,0 @@
-# Sentiment Evolution Tracker
-
-## Purpose
-
-A Claude Desktop extension that tracks customer sentiment over time with persistent memory. Claude gains the ability to remember customer histories across sessions.
-
-## The Gap
-
-Standard Claude usage:
-- Brilliant single-turn analysis ✅
-- Forgets everything after the chat ❌
-- Cannot compare customers across conversations ❌
-- Lacks historical reporting ❌
-
-## The Solution
-
-This MCP server exposes six domain-specific tools:
-
-### Realtime Analysis Tools
-1. **Sentiment Evolution** – Detects whether sentiment is improving, declining, or stable.
-2. **Risk Signal Detection** – Flags pricing pressure, competitor mentions, frustration markers.
-3. **Next Action Prediction** – Suggests whether to escalate, retain, or close.
-
-### Historical Intelligence Tools
-4. **Customer History** – Retrieves stored analyses for a customer.
-5. **High-Risk Customers** – Lists accounts trending toward churn.
-6. **Portfolio Statistics** – Summarizes overall health metrics.
-
-## High-Level Flow
-
-```
-User describes customer interaction
-                ↓
-Claude selects the appropriate tool
-                ↓
-MCP server runs analysis or database query
-                ↓
-Results persist to SQLite
-                ↓
-Claude returns structured insights
-```
-
-## Practical Example
-
-**Prompt:**
-```
-Customer messages:
-- "Service is excellent"
-- "Pricing is higher than the competition"
-- "Considering a switch"
-
-Are they at risk?
-```
-
-**Automatic pipeline:**
-1. Sentiment shifts from 57 → 43/100 (trend `DECLINING`).
-2. Signals highlight competitor mention and potential churn.
-3. Recommended action: `MONITOR_CLOSELY` with 65% confidence.
-4. Analysis is stored in the database.
-5. If risk > 70%, an alert is created.
-
-**Claude responds:**
-```
-Medium risk detected:
-- Declining sentiment trajectory
-- Explicit competitor comparison
-- Action: urgent outreach and pricing review
-```
-
-## Why It Matters
-
-**Without MCP**
-- Claude only reflects the current conversation.
-- Historical context is lost.
-- No portfolio-level reporting.
-
-**With MCP**
-- Persistent memory across customers ✅
-- Trend comparisons over time ✅
-- Automated alert generation ✅
-- Portfolio dashboards ✅
-
-## Real-World Scenario
-
-**Day 1 – New customer "Juan García"**
-```
-Sentiment: STABLE at 70/100
-Risk: Low
-Record stored in SQLite
-```
-
-**Day 7 – Follow-up from Juan García**
-```
-Message: "Pricing is too high; I might switch"
-Sentiment drops to 43/100 → trend DECLINING
-Risk moves to MEDIUM
-Alert generated automatically
-```
-
-**Outcome:**
-- Detects customer sentiment shifts immediately.
-- Maintains full conversation history.
-- Surfaces alerts before churn occurs.
-- Enables data-driven retention strategies.
-
-## Technology Stack
-
-- **Python 3.10** for orchestration.
-- **Anthropic MCP** for Claude integration.
-- **SQLite** for persistent storage.
-- **TextBlob + NLTK** delivering lightweight NLP.
-
-## Feature Highlights
-
-✅ Automated sentiment scoring  
-✅ Risk signal detection  
-✅ Churn prediction with recommended actions  
-✅ Persistent customer histories  
-✅ Alert generation when thresholds are crossed  
-✅ Portfolio-level reporting  
-
-## Quick Installation
-
-1. `pip install -r requirements.txt`
-2. `python -m nltk.downloader punkt`
-3. Register the server in Claude Desktop config.
-4. Restart Claude to apply.
-
-## Current Limitations
-
-- Lexical sentiment model (no deep transformer yet).
-- Optimized for English and Spanish.
-- Probabilistic scoring; not deterministic.
-- Works best with conversations ≥ 3 messages.
-
-## Roadmap
-
-- Transformer-based sentiment and emotion detection.
-- Web dashboard for live monitoring.
-- Realtime notifications (Slack/email/webhook).
-- Expanded multilingual support.
-- Fine-grained emotion tagging.
-
-## Value Proposition
-
-1. **Data persistence** – Claude remembers customers across sessions.
-2. **Historical analytics** – Track trends instead of snapshots.
-3. **Automation** – Alerts and predictions run autonomously.
-4. **Scalability** – Moves from single-use analysis to enterprise tooling.
-
-## Closing Thoughts
-
-Sentiment Evolution Tracker is a production-ready MCP server proving how custom tools can elevate Claude from conversational analysis to strategic customer intelligence.
-
----
-
-**Ready to try it?** The repository ships with demo data and scripts.
-
----
-
-Rubén Reyes · November 2025 · v1.0