MCP Insightengine

MCP-InsightEngine

Project Overview

MCP-InsightEngine is a large language model (LLM)-based file analysis and insight generation tool built using Model Context Protocol (MCP). It intelligently analyzes structured and unstructured files (CSV, JSON, PDF, and text) to extract actionable insights, detect anomalies, and generate summaries. This project demonstrates expertise in AI/ML pipeline development, LLM integration, and scalable data intelligence.

Architecture diagram

graph TD
    A[AI Client] -->|Sends Request| B[MCP Server]
    B --> C[Parser]
    C --> D[Insights Engine]
    D --> E[Visualizer]
    E -->|Returns Response| A

🧩 High-Level Architecture

• At its core, the MCP-InsightEngine comprises several key components:

• MCP Server (mcp_server.py): Acts as the central server that listens for incoming requests from AI clients.

• Parser (parser.py): Handles the interpretation of incoming data, ensuring it's in a format that the server can process.

• Insights Engine (insights.py): Processes the parsed data to extract meaningful insights or perform specific actions.

• Visualizer (visualizer.py): Generates visual representations of the insights for easier understanding and analysis.

🔄 Data Flow

• Client Request: An AI client sends a request to the MCP server, typically in the form of an HTTP request.

• Parsing: The parser.py module processes the incoming request, extracting relevant data and converting it into a structured format.

• Insight Generation: The structured data is passed to the insights.py module, which analyzes it to generate insights or perform actions.

• Visualization: The visualizer.py module takes the generated insights and creates visual representations, such as graphs or charts.

• Response: The visualizations and insights are sent back to the AI client as a response.

Key Features

• LLM-Powered File Analysis: Understands context and content beyond keyword matching.

• Multi-Format File Support: CSV, JSON, PDF, and plain text.

• Actionable Insights: Generates summaries, trend highlights, and anomaly detection.

• Modular & Scalable Architecture: Built on MCP for extensible AI pipelines.

• Interactive Frontend: Streamlit interface for real-time file upload and analysis.

Technologies and Skills

• Programming Languages: Python

• AI/ML: OpenAI GPT-4, custom LLMs, natural language processing (NLP)

• Backend & APIs: FastAPI

• Data Processing: Pandas, PyMuPDF

• Frontend: Streamlit

• Dependency Management: uv package manager

• Software Development Skills: Scalable AI pipelines, modular architecture, LLM integration, data intelligence

Installation

Clone the repository and install dependencies using uv:

git clone https://github.com/anulsasidharan/MCP-InsightEngine.git
cd MCP-InsightEngine
uv add -r requirements.txt