Assignment 1.5: MCP on HiPerGator

CIS 6930 Data Engineering - Spring 2026

Overview

This repository contains starter code for the in-class MCP activity on HiPerGator. You will build an MCP server that processes the dair-ai/emotion dataset from Hugging Face.

Setup on HiPerGator

1. Clone this repository

cd /blue/cis6930/YOUR_GATORLINK
git clone https://github.com/YOUR_USERNAME/cis6930sp26-assignment1.5.git
cd cis6930sp26-assignment1.5

2. Create environment file

cp .env.example .env
# Edit .env with your Hugging Face token

3. Install dependencies

module load mamba
uv sync
source .venv/bin/activate

Running the MCP Inspector

Option 1: HiPerGator Desktop (Recommended)

Using HiPerGator Desktop is much easier and avoids SSH tunneling complexity.

1. Go to https://ondemand.rc.ufl.edu
2. Navigate to Interactive Apps > HiPerGator Desktop
3. Request a session (1 hour, 4 GB memory, 2 CPUs)
4. Once the desktop launches, open a terminal
5. Run:

   cd /blue/cis6930/YOUR_GATORLINK/cis6930sp26-assignment1.5
   module load mamba
   source .venv/bin/activate
   mcp dev server.py
   

6. Open Firefox in the virtual desktop and go to http://localhost:6274

Option 2: SSH Tunneling from Your Laptop

If you prefer to use your local browser, you'll need to set up SSH tunneling.

Step 1: Start the MCP Inspector on a Compute Node

Run this command to start an interactive job with the MCP inspector:

ssh hpg "srun --partition=hpg-turin --account=cis6930 --qos=cis6930 \
    --cpus-per-task=4 --ntasks=1 --mem-per-cpu=4gb --time=1:00:00 \
    bash -c 'cd /blue/cis6930/YOUR_GATORLINK/cis6930sp26-assignment1.5 && \
    module load mamba && source .venv/bin/activate && mcp dev server.py'"

Step 2: Find Your Compute Node Name

In another terminal, find which compute node your job is running on:

squeue -u $USER

Look for the node name in the NODELIST column (e.g., c0702a-s2).

Step 3: Set Up the SSH Tunnel

The MCP Inspector runs on two ports:

• 6274 - Web interface
• 6277 - Proxy server

Open a new terminal on your laptop and run:

ssh -L 6274:localhost:6274 -L 6277:localhost:6277 \
    -J YOUR_GATORLINK@hpg.rc.ufl.edu YOUR_GATORLINK@COMPUTE_NODE

Example:

ssh -L 6274:localhost:6274 -L 6277:localhost:6277 \
    -J jsmith@hpg.rc.ufl.edu jsmith@c0702a-s2

The -J flag (ProxyJump) connects through the login node directly to the compute node.

Step 4: Open the Inspector

Open your browser and go to the url that was output by the the dev server. Is should look like http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=e2a71ba1e83a76dd0ea24fed08b1d62413d5837fbea81cbc41a9233ae169f989 : http://localhost:6274?MCP_PROXY_AUTH_TOKEN={CODE}

You should see the MCP Inspector interface:

Passwordless SSH Login

To avoid entering your password multiple times, set up SSH keys for HiPerGator: https://docs.rc.ufl.edu/access/ssh_keys/

Troubleshooting

Problem	Solution
"Connection refused"	Ensure you're using -J (ProxyJump) and forwarding both ports (6274 and 6277)
"Address already in use"	Kill processes: `lsof -ti:6274
Host key verification failed	Add -o StrictHostKeyChecking=no to the SSH command
Page won't load	Verify mcp dev is still running on the compute node

Using the MCP Inspector

1. Click the Tools tab in the left sidebar
2. Select a tool from the list (e.g., get_sample)
3. Fill in the parameter values
4. Click Run Tool
5. View the JSON response in the output panel

Tools to Test

Tool	Parameters
get_sample	n: 3
count_by_emotion	emotion: "joy"
search_text	query: "happy", limit: 5
analyze_emotion_distribution	(no parameters)

Dataset

The dair-ai/emotion dataset contains English Twitter messages labeled with six emotions:

Label	Emotion
0	sadness
1	joy
2	love
3	anger
4	fear
5	surprise

Submission

After completing the activity, submit to Canvas:

1. Your outputs.txt file with tool outputs
2. A brief reflection (2-3 sentences)

Resources

• MCP Python SDK
• Hugging Face Datasets
• HiPerGator Documentation
• HiPerGator SSH Keys