Provides a send_email tool that sends emails through Gmail SMTP using configured Gmail account credentials.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Assistant Toolkit what's 128 * 37, and what time is it in Asia/Karachi?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that exposes a small toolbox (math, web search, email, and time tools) to an AI agent, plus a client that connects a Groq-hosted LLM (via LangChain + LangGraph) to that server so it can decide, on its own, which tool to call to answer a question.
Built as a course project, extending the MCP server/agent pattern taught in class into a fuller, better-documented project.
You
|
v
+-------------------------------+ MCP over HTTP +----------------------------+
| client.py | <----------------------> | server.py |
| - LangGraph ReAct agent | | - FastMCP tool server |
| - Groq LLM (reasoning) | | - add / subtract / |
| - dynamically wraps every | | multiply / divide / |
| tool the server exposes | | square_root |
+-------------------------------+ | - google_search |
| - send_email |
| - get_current_time |
+----------------------------+
| |
v v
SerpAPI Gmail SMTPThe client never hard-codes what a tool's inputs look like: it asks the
server for its tool list (name, description, JSON Schema) and builds a
matching Pydantic model automatically. Add a new tool to server.py and the
client picks it up on the next run with no changes needed on its side.
Related MCP server: Okta Calculator MCP Server
mcp_project/
|-- server.py # MCP tool server (run this first)
|-- client.py # Agent client (run this second)
|-- test_tools.py # Direct smoke test of the server's tools (no LLM needed)
|-- requirements.txt
|-- .env.example # Copy to .env and fill in your own keys
|-- .gitignore
|-- README.md
`-- IMPLEMENTATION.md # Write-up: design decisions, architecture, testingCreate and activate a virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activateInstall dependencies
pip install -r requirements.txtConfigure environment variables
cp .env.example .envThen open .env and fill in:
Variable | Required for | Where to get it |
| the agent's LLM | |
|
| |
|
|
Every tool degrades gracefully with a clear error message if its key is missing, so you can run the project with only some keys set.
Terminal 1 -- start the tool server:
python server.pyTerminal 2 -- verify the tools directly (optional but recommended before your demo):
python test_tools.pyTerminal 2 -- start the agent:
python client.pyThis opens an interactive chat. Try:
What is 128 * 37?
What's the current time in Asia/Karachi?
Search for the latest news on the Model Context Protocol
Email a summary of the last answer to you@example.com
Or ask a one-off question without the interactive loop:
python client.py "What's 12 * 8, and what time is it in Asia/Karachi?"This server cannot be deployed
Time and date math for AI agents: Unix timestamp conversion, DST-correct time zone conversion, durations, epoch arithmetic, cron schedules, and holiday countdowns. Eight tools, no key.
Gateway between LLM agents and world data through eight tools and a bundled endpoint catalog.
Deterministic time tools for AI agents: timezone conversion, business-day math, cron interpretation.
500+ deterministic tools for AI agents: math, conversion, validation, hashing, encoding, date/time.
