Genai Sales Insights Agent

🚀 GenAI Sales Insights Agent + MCP Dataset Preview Server

A GenAI engineering project combining an LLM - powered insights agent and a minimal MCP server to analyze financial sales data and expose dataset previews.

🚀 Quick Start

1. Run the Python Insights Agent

pip install -r requirements.txt

Create a .env file:

OPENAI_API_KEY=your_key_here

Run the script:

python sales_insights_agent.py

Outputs:

• HTML insights printed to console
• sales_insights_report.html generated

Note: A sample output file is included in the repo.

2. Run the MCP Server

cd mcp_insights_server
npm install
npm start

Starts a minimal MCP server over STDIO with the dataset_preview tool available.

✨ Features

This repository contains two main components:

1. AI Insights Agent (Python)

A pipeline that:

• Loads structured sales data
• Performs quantitative analysis with Pandas
• Builds a summary that captures core metrics
• Sends the summary to an LLM
• Receives structured HTML insights
• Generates a clean, styled report preview

2. MCP Server (Node.js)

A lightweight Model Context Protocol server exposing a single tool: dataset_preview. This tool:

• Reads the same sales CSV from the Python project
• Returns the first 8 lines, formatted as text
• Allows an AI agent to “inspect” enterprise datasets before reasoning over them

🔧 Technical Details

Architecture Rationale

Enterprises rarely allow LLMs direct filesystem access. Instead, they rely on:

• Tool - based access
• Strict interfaces
• Auditable data flow
• Separation of reasoning and data layers

This project follows that pattern:

Python Agent: Reasoning Layer

Performs analysis, generates summaries, produces HTML insights.

MCP Server: Data Access Layer

Exposes structured, restricted access to the dataset through a tool.

Repository Structure

genai-sales-insights-agent/
│
├── ai_insights_agent/
│ ├── data/
│ │ └── sales_sample.csv
│ ├── sales_insights_agent.py
│ ├── sales_insights_report.html
│ ├── requirements.txt
│ └── .env (ignored)
│
└── mcp_insights_server/
├── index.mjs
├── package.json
└── package-lock.json

AI Insights Agent (Python)

How it works

1. Load Data
   The script loads and parses financial sales data (regions, products, revenue, cost, units_sold).
2. Generate Pandas Summary
   The agent computes:
   • Statistical summaries
   • Profit calculations
   • Revenue/profit grouped by region
   • Revenue/profit grouped by product
3. Send Prompt to LLM
   The summary is passed to an OpenAI model (gpt-4o-mini) with strict instructions to output HTML only.
4. Generate HTML Report
   The returned HTML fragment is inserted into a styled template. The final file: sales_insights_report.html. This produces a clean, readable dashboard - like insights page similar to what an internal analytics team might generate.

MCP Server (Node.js)

The MCP (Model Context Protocol) allows LLM - based agents to call tools through a standard interface. This is meant to simulate how internal GenAI agents can safely inspect datasets before using them as context. This project demonstrates the simplest working version of such a tool.

📄 License

No license information provided in the original README.

📋 Author

Rawan Morshed