Raj Assistant MCP
A modular AI Assistant built using the Model Context Protocol that provides tools for calculator, weather, news, AI chat, currency conversion, document processing, and future RAG capabilities.
README
š¤ 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
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.envto 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
addsubtractmultiplydividepercentage
Date/time
current_datecurrent_timecurrent_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/
āāā .env.example
āāā config.py
āāā server.py
āāā requirements.txt
āāā README.md
āāā tools/
ā āāā calculator.py
ā āāā chat.py
ā āāā currency.py
ā āāā datetime.py
ā āāā documents.py
ā āāā news.py
ā āāā rag.py
ā āāā weather.py
āāā services/
ā āāā ai_service.py
ā āāā currency_service.py
ā āāā document_service.py
ā āāā rag_service.py
ā āāā news_service.py
ā āāā weather_service.py
āāā 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:
- discovers tools with
tools/list - sends the available tools to the model
- lets the model choose a tool
- calls the selected MCP tool with
tools/call - feeds the result back to the model
- 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
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