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: leftbrain
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.
Pay-per-call tools for AI agents, paid in USDC on Base or Solana. Web search, page and PDF reading, crypto news, Hyperliquid perps, market pulse, Chainlink price history, token checks, SEC filings, weather and more. Free calls every day, and a failed call costs nothing. Works with Tollkit Wallet.
Deterministic time tools for AI agents: timezone conversion, business-day math, cron interpretation.
