🤖 Raj Assistant MCP

A modular Model Context Protocol (MCP) server built with Python. This project is designed to work with MCP Inspector and other MCP-compatible clients.

What is implemented

• Calculator: add, subtract, multiply, divide, percentage

• Date/time with IANA timezones

• Current weather using Open-Meteo

• News using NewsData.io

• Currency conversion using Frankfurter

• Gemini AI chat

• PDF, DOCX, TXT, MD, CSV, XLSX and basic image file reading

• Local RAG document indexing and semantic search with ChromaDB

• Clean service/tool separation

• Environment-based configuration

• Basic tests

Related MCP server: MCP Server For Local

Architecture

MCP Client / Inspector
        |
        v
   server.py
        |
        +-------------------+
        |                   |
     MCP Tools          Services
        |                   |
        +---------+---------+
                  |
       External APIs / Chroma
                  |
              Local files

1. Setup on Windows

python -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install -r requirements.txt
copy .env.example .env

Edit .env and add your own NEWS_API_KEY and GEMINI_API_KEY.

Security: The old uploaded project contained API keys in .env. Rotate/revoke those keys and never commit .env to Git.

2. Run normally

python server.py

The MCP server uses stdio, so it may appear to wait without printing a normal web page.

3. Run MCP Inspector

mcp dev server.py

Then open the Inspector URL printed in the terminal.

If you get ModuleNotFoundError, activate the virtual environment in the same PowerShell window before running the command.

4. Tools

Calculator

• add

• subtract

• multiply

• divide

• percentage

Date/time

• current_date

• current_time

• current_datetime

Default timezone is Asia/Kolkata. You can pass another IANA timezone such as UTC or America/New_York.

Weather

get_current_weather(latitude=21.1702, longitude=72.8311)

Open-Meteo does not require an API key.

News

Requires NEWS_API_KEY.

top_news(topic="technology", country="in", limit=5)

Currency

convert_currency(amount=100, from_currency="USD", to_currency="INR")

Uses Frankfurter's public exchange-rate API.

AI chat

Requires GEMINI_API_KEY.

chat(message="Explain MCP in simple words")

Documents

read_document(path="C:\\path\\to\\file.pdf")
index_document(path="C:\\path\\to\\file.pdf")
search_knowledge(query="What is this document about?", top_k=5)

Supported: PDF, DOCX, TXT, MD, CSV, XLSX, JSON and basic image metadata.

5. Project structure

project_mcp/
├── .env.example
├── config.py
├── server.py
├── requirements.txt
├── README.md
├── tools/
│   ├── calculator.py
│   ├── chat.py
│   ├── currency.py
│   ├── datetime.py
│   ├── documents.py
│   ├── news.py
│   ├── rag.py
│   └── weather.py
├── services/
│   ├── ai_service.py
│   ├── currency_service.py
│   ├── document_service.py
│   ├── rag_service.py
│   ├── news_service.py
│   └── weather_service.py
└── tests/

Next stage: AI Agent

The MCP server is now a strong tool layer. An AI Agent should normally live in an MCP client/agent application that:

1. discovers tools with tools/list

2. sends the available tools to the model

3. lets the model choose a tool

4. calls the selected MCP tool with tools/call

5. feeds the result back to the model

6. repeats until the final answer is ready

This keeps the MCP server focused on reliable capabilities instead of mixing agent orchestration into every tool.

License

MIT