Python MCP Agent Chatbot

An Agentic AI classroom project built completely in Python. Groq handles model reasoning while a local MCP server supplies tools.

Architecture

Browser -> FastAPI (Python) -> Groq -> MCP Client -> MCP Server (Python) -> Tool

Related MCP server: mcp-datecalc

Files

• app.py — FastAPI web backend, Groq agent loop, and MCP client

• mcp_server.py — Python MCP server containing three tools

• test_mcp.py — MCP discovery and tool-call test

• index.html — browser chat interface

• pyproject.toml — Python dependencies

Run on Windows

uv sync --cache-dir .uv-cache
uv run --cache-dir .uv-cache uvicorn app:app --reload

Open http://127.0.0.1:8000.

MCP ko Groq API call ke baghair test karne ke liye:

uv run --cache-dir .uv-cache python test_mcp.py

Try these questions:

• 25 * 8 + 10 calculate karo

• Karachi mein abhi kya time hai?

• Ahmed ki attendance kya hai?

MCP flow

1. Functions in mcp_server.py are registered with @mcp.tool().

2. app.py launches that MCP server as a local Python subprocess using stdio.

3. The MCP client calls list_tools() so Groq can see the available tools.

4. Groq selects a tool and generates its arguments.

5. The MCP client calls call_tool() and returns the tool result to Groq.

6. Groq converts the result into a natural-language answer.

The .env file contains the Groq key and is intentionally excluded from Git.