MCP_Server_Mahendran

mahendran1387a/MCP_Server_Mahendran

3.2

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This project integrates LangChain, Ollama, and a Model Control Plane (MCP) server to provide a robust platform for building LLM applications with additional tools for calculations and weather information.

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LangChain + Ollama + MCP Server

A powerful integration combining LangChain, Ollama, and a Model Control Plane (MCP) server with multiple intelligent tools.

Features

  • LangChain Integration: Leverages LangChain for building LLM applications
  • Ollama Support: Uses local Ollama models for privacy and control
  • MCP Server: Implements a Model Control Plane with 8 powerful tools:
    • Calculator: Perform arithmetic operations (add, subtract, multiply, divide)
    • Weather: Get weather information for any city (mock data for demo)
    • Gold Price: Get live market gold prices in multiple currencies (USD, EUR, GBP, INR)
    • Email: Send emails with subject and body to recipients
    • RAG (Retrieval-Augmented Generation): Upload documents and query them with semantic search
    • Code Execution: Execute Python code safely with output capture
    • Web Scraping: Extract text and links from web pages
    • File Operations: Read, write, list files and directories
  • Modern Web Interface: Beautiful responsive UI with dark mode and toast notifications
  • Interactive CLI: User-friendly command-line interface
  • Demo Mode: Pre-configured examples to showcase capabilities
  • Interactive Visualizations: Two-tab web interface showing step-by-step and animated flows
  • Document Intelligence: RAG system with vector database for document search

Architecture

┌─────────────────────────────────────────────────────────┐
│                     Main Application                     │
│                      (main.py)                          │
└─────────────────┬───────────────────────────────────────┘
                  │
                  ▼
┌─────────────────────────────────────────────────────────┐
│            LangChain MCP Client                         │
│          (langchain_mcp_client.py)                      │
│                                                         │
│  ┌──────────────┐           ┌───────────────┐         │
│  │   LangChain  │◄─────────►│  MCP Wrapper  │         │
│  │   + Ollama   │           │               │         │
│  └──────────────┘           └───────┬───────┘         │
└────────────────────────────────────┼───────────────────┘
                                     │
                                     ▼
┌─────────────────────────────────────────────────────────┐
│                   MCP Server                            │
│                (mcp_server.py)                          │
│                                                         │
│  ┌──────────┐  ┌────────┐  ┌─────────┐  ┌──────┐     │
│  │Calculator│  │Weather │  │GoldPrice│  │Email │     │
│  │   Tool   │  │  Tool  │  │  Tool   │  │ Tool │     │
│  └──────────┘  └────────┘  └─────────┘  └──────┘     │
│                                                         │
│  ┌──────────────────────────────────────────────┐     │
│  │          RAG Query Tool                       │     │
│  │      (Document Search & Retrieval)           │     │
│  └────────────────┬─────────────────────────────┘     │
└───────────────────┼───────────────────────────────────┘
                    │
                    ▼
         ┌────────────────────────┐
         │    RAG System          │
         │  (rag_system.py)       │
         │                        │
         │  • Document Upload     │
         │  • Text Extraction     │
         │  • Chunking            │
         │  • Vector Search       │
         └──────────┬─────────────┘
                    │
                    ▼
         ┌────────────────────────┐
         │     ChromaDB           │
         │  (Vector Database)     │
         └────────────────────────┘

Prerequisites

  1. Python 3.9+
  2. Ollama installed and running

Installation

  1. Clone the repository:
git clone <repository-url>
cd MCP_Server_Mahendran
  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Verify Ollama is running:
ollama list

Usage

Quick Start

Run the main application:

python main.py

Choose from:

  1. Interactive Mode: Ask questions in real-time
  2. Demo Mode: See pre-configured examples

Interactive Mode

python main.py
# Select option 1

💬 You: What is 25 multiplied by 4?
# The assistant will use the calculator tool to compute the result

💬 You: What's the weather in Paris?
# The assistant will use the weather tool to fetch weather data

💬 You: Calculate 100 divided by 5
# Another calculation example

Demo Mode

python main.py
# Select option 2
# Watch automated demonstrations of all features

Direct Client Usage

You can also use the client directly in your own scripts:

import asyncio
from langchain_mcp_client import LangChainMCPClient

async def main():
    client = LangChainMCPClient(model_name="llama3.2")

    try:
        await client.initialize()
        result = await client.process_query("What is 15 + 27?")
        print(result)
    finally:
        await client.cleanup()

asyncio.run(main())

Project Structure

MCP_Server_Mahendran/
├── main.py                       # Main CLI application
├── langchain_mcp_client.py       # LangChain + Ollama + MCP integration
├── mcp_server.py                 # MCP server with 5 tools
├── rag_system.py                 # RAG system with ChromaDB
├── web_server.py                 # Flask web server
├── requirements.txt              # Python dependencies
├── .gitignore                    # Git ignore rules
├── README.md                     # This file
├── RAG_README.md                 # Detailed RAG documentation
├── WEB_FRONTEND_README.md        # Web interface documentation
├── templates/
│   └── index.html                # Web frontend with RAG interface
├── visualization.html            # Interactive flow visualization
├── uploads/                      # Temporary upload directory
└── rag_db/                       # ChromaDB vector database

MCP Tools

Calculator Tool

Performs basic arithmetic operations.

Parameters:

  • operation: "add", "subtract", "multiply", or "divide"
  • a: First number
  • b: Second number

Example:

{
  "tool": "calculator",
  "arguments": {
    "operation": "multiply",
    "a": 25,
    "b": 4
  }
}

Weather Tool

Gets weather information for a city (mock data for demonstration).

Parameters:

  • city: City name (required)
  • units: "celsius" or "fahrenheit" (default: "celsius")

Example:

{
  "tool": "weather",
  "arguments": {
    "city": "Paris",
    "units": "celsius"
  }
}

Gold Price Tool

Gets live market gold prices in multiple currencies.

Parameters:

  • currency: "USD", "EUR", "GBP", or "INR" (default: "USD")

Example:

{
  "tool": "gold_price",
  "arguments": {
    "currency": "USD"
  }
}

Sample Output:

💰 Live Gold Price
────────────────────────────────
Price: USD 2,050.25 per troy ounce
24h Change: +0.75% (+USD 15.23)
Currency: USD
Updated: 2025-11-14 10:30:00

Market Status: 🟢 Open

Email Tool

Send emails with subject and body to recipients (simulated for demo).

Parameters:

  • to: Recipient email address (required)
  • subject: Email subject line (required)
  • body: Email body content (required)

Example:

{
  "tool": "send_email",
  "arguments": {
    "to": "user@example.com",
    "subject": "Gold Price Alert",
    "body": "Current gold price is $2,050 per ounce"
  }
}

Sample Output:

📧 Email Sent Successfully!
────────────────────────────────
To: user@example.com
Subject: Gold Price Alert
Sent: 2025-11-14 10:30:00

Message Preview:
Current gold price is $2,050 per ounce

Status: ✅ Delivered

RAG (Retrieval-Augmented Generation) Tool

Search uploaded documents using semantic similarity and natural language queries.

Parameters:

  • query: The question or search query (required)
  • n_results: Number of relevant documents to retrieve (optional, default: 3)

Example:

{
  "tool": "rag_query",
  "arguments": {
    "query": "What does the document say about AI?",
    "n_results": 3
  }
}

Sample Output:

📚 RAG Query Results
────────────────────────────────
Query: What does the document say about AI?
Found: 3 relevant document(s)

Result #1 (Relevance: High)
────────────────────────────────
Artificial Intelligence (AI) is the simulation of human
intelligence processes by machines, especially computer systems...

Metadata: ai_guide.pdf | Length: 450 chars
────────────────────────────────
💡 Tip: You can use this information to answer your question!

Features:

  • Upload documents via web interface (TXT, PDF, DOC, DOCX, JSON, MD, CSV)
  • Semantic search using vector embeddings
  • Automatic document chunking for better retrieval
  • Relevance scoring and ranking
  • ChromaDB vector database for efficient search

For detailed RAG documentation, see

Configuration

Changing the Ollama Model

Edit the model name in main.py or langchain_mcp_client.py:

client = LangChainMCPClient(model_name="llama3.2")  # Change to your preferred model

Available models (install with ollama pull <model>):

  • llama3.2
  • llama3.1
  • mistral
  • phi3
  • And more...

Adding Custom Tools

To add new tools to the MCP server:

  1. Add the tool definition in mcp_server.py in the list_tools() method
  2. Implement the tool logic as a new method
  3. Add the tool handler in the call_tool() method

Example:

# In list_tools()
Tool(
    name="my_custom_tool",
    description="Description of what it does",
    inputSchema={
        "type": "object",
        "properties": {
            "param1": {
                "type": "string",
                "description": "Parameter description"
            }
        },
        "required": ["param1"]
    }
)

# Implement the tool
async def my_custom_tool(self, arguments: dict) -> list[TextContent]:
    param1 = arguments.get("param1")
    # Your logic here
    return [TextContent(type="text", text=f"Result: {param1}")]

# In call_tool()
elif name == "my_custom_tool":
    return await self.my_custom_tool(arguments)

Web Interface

Starting the Web Server

Launch the beautiful web interface with RAG support:

python web_server.py

Then open your browser to: http://localhost:5000

Web Interface Features

✅ Interactive Chat

  • Real-time conversation with AI agent
  • Beautiful gradient UI design
  • Message history and timestamps

✅ RAG Document Upload

  • Drag-and-drop file upload
  • Support for 7 file formats (TXT, PDF, DOC, DOCX, JSON, MD, CSV)
  • Real-time upload status
  • Database statistics display

✅ Tool Integration

  • Quick action buttons for all 5 tools
  • Tool sidebar with examples
  • One-click query templates

✅ Session Management

  • Persistent conversations
  • Clear conversation history
  • Per-session state management

For detailed web interface documentation, see

Documentation

This project includes comprehensive documentation:

  • (this file) - Main project documentation
  • - Complete RAG system guide
    • Document upload and management
    • Query examples and best practices
    • API documentation
    • Troubleshooting guide
    • Advanced usage patterns
  • - Web interface guide
    • Quick start instructions
    • API endpoints
    • Customization options
    • Deployment guide

Troubleshooting

Ollama Connection Issues

If you get connection errors:

  1. Make sure Ollama is running: ollama serve
  2. Check if the model is installed: ollama list
  3. Pull the model if needed: ollama pull llama3.2

MCP Server Not Starting

  1. Check if port is already in use
  2. Verify Python version is 3.9+
  3. Reinstall dependencies: pip install -r requirements.txt --force-reinstall

Import Errors

Make sure you're in the virtual environment:

source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

RAG Issues

No documents found:

  1. Check if documents were uploaded: Click "🔄 Refresh Stats"
  2. Verify file format is supported
  3. Check server logs for processing errors

Upload fails:

  1. Ensure file is under 16MB
  2. Check file extension is supported
  3. Verify disk space for ./rag_db directory

Low relevance results:

  1. Rephrase query using document terminology
  2. Upload more relevant documents
  3. Try requesting more results (n_results=5)

For detailed RAG troubleshooting, see

Development

Running Tests

# Add your tests here
pytest tests/

Code Style

Format code with black:

pip install black
black *.py

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

License

MIT License - feel free to use this project for any purpose.

Acknowledgments

  • LangChain - Framework for LLM applications
  • Ollama - Local LLM inference
  • MCP - Model Context Protocol
  • ChromaDB - Vector database for RAG
  • Flask - Web framework for Python

Support

For issues and questions:

Made with ❤️ for the AI community