This is the repo for testing my custom MCP-server

COMP3900 project installer server

comp3900_server.py is a dedicated MCP server for the project at https://github.com/arctic-cheetah/COMP3900-Project. It can:

• clone the fixed repository onto the machine running the MCP server;

• expose the project README and bounded project-file reads to the LLM;

• check prerequisites, including whether Playwright's Chromium is usable;

• install the backend, Playwright Chromium, and frontend locally;

• build/start the documented Docker Compose stack; or

• optionally install the cloudflared client (see below).

The download goes to ./COMP3900-Project by default. To use another location, set COMP3900_PROJECT_DIR to an explicit project directory.

Install this MCP server's dependency first:

python3 -m pip install -r requirements.txt

Run it over stdio:

mcp run comp3900_server.py:mcp

Or launch it directly:

python3 comp3900_server.py

In an MCP client, select the install_comp3900 prompt for a guided workflow, or call setup_project directly. Example arguments for a local installation:

{
  "method": "local",
  "install_browser": true,
  "install_browser_system_dependencies": true,
  "start_services": false,
  "install_cloudflare_tunnel_client": false
}

For Docker, use "method": "docker"; set "start_services": true to run the stack in the background after building it.

Playwright

The URL detector's HTML fetch engine runs on Playwright, so it is a genuine requirement: backend/pyproject.toml lists playwright, and both backend/Dockerfile and operational-install-manual.md run playwright install --with-deps chromium.

install_browser_system_dependencies now defaults to true to match that documented step. It shells out to the platform package manager for Chromium's shared libraries, so on a host where the server does not already run as root it can prompt for elevation. Set it to false if you would rather install those libraries yourself. check_prerequisites reports url_detector_ready, which is true only when both the Playwright module and its Chromium browser resolve.

cloudflared

install_cloudflared downloads the official release binary into <checkout>/.tools/cloudflared and reports its sha256. It is off by default in install_project and setup_project; pass "install_cloudflare_tunnel_client": true to include it.

Two caveats worth knowing:

• Cloudflare is not a dependency of the COMP3900 project. It appears nowhere in the README, the install manual, or any manifest. The only occurrences in the repository are domain strings such as cdnjs.cloudflare.com inside the ML training CSVs under backend/ml/data/.

• The server installs the client only. It never authenticates to Cloudflare and never starts a tunnel, because running one publishes a local service on the public internet. That remains a deliberate human step.

Installing into the checkout keeps the operation unprivileged: no system package manager runs and nothing is written outside the project directory.

Related MCP server: homelab-mcp

Instructions

From the project directory, launch the interactive MCP Inspector:

mcp dev server.py

The command prints a local Inspector URL. Open it, connect, select the add tool, and provide:

{
  "a": 1,
  "b": 2
}

The result should be 3.

To run it as a normal stdio MCP server for an MCP client:

mcp run server.py:mcp

Running python server.py currently exits immediately because the file only defines the server. To support that command, append:

if __name__ == "__main__":
    mcp.run()

Then run

python3 server.py

If MCP is not installed on another machine:

python3 -m pip install "mcp[cli]"

Then

mcp dev server.py

Deployments:

This is the description of what the code block changes: Adding the connection guide (STDIO and HTTP options) to README.md after the existing content.

This is the code block that represents the suggested code change:

Connecting to ChatGPT Desktop

Option 1: Connect through STDIO (recommended)

Because the server currently runs inside WSL, fill the desktop form as follows:

• Name: MCP_test

• Type: STDIO

• Command to launch: wsl.exe

• Arguments: add each item separately, in this order:

  --cd
  /home/khalifa/MCP-server
  --exec
  /home/khalifa/pythonPackages/bin/mcp
  run
  server.py:mcp

• Environment variables: leave empty

• Working directory: leave empty

If the wrong WSL distribution is selected, insert these arguments first:

--distribution
Ubuntu

Replace Ubuntu with the name reported by:

wsl.exe --list --verbose

So effectively it looks like:

• Arguments: add each item separately, in this order:

  --distribution
  kali-linux
  --cd
  /home/khalifa/MCP-server
  --exec
  /home/khalifa/pythonPackages/bin/mcp
  run
  server.py:mcp

Save the server and restart the desktop app. In a chat, enter:

/mcp

You should see MCP_test and its add tool. Try:

Use the MCP_test add tool to add 17 and 25.

The official OpenAI documentation confirms that the desktop app supports both local STDIO processes and Streamable HTTP servers. It also requires restarting after saving the configuration. OpenAI MCP documentation

Option 2: Run the server over HTTP

Stop mcp dev, then run:

cd /home/khalifa/MCP-server
mcp run server.py:mcp --transport streamable-http

The default MCP endpoint is:

http://127.0.0.1:8000/mcp

In the desktop form:

• Name: MCP_test_http

• Type: Streamable HTTP

• URL: http://127.0.0.1:8000/mcp

There is no launch command or arguments for this mode. The HTTP server must already be running.

If port 8000 is occupied, add this to server.py:

if __name__ == "__main__":
    mcp.run(
        transport="streamable-http",
        host="127.0.0.1",
        port=8001,
    )

Then run:

python3 server.py

Use this desktop URL:

http://127.0.0.1:8001/mcp

Calling it with an HTTP request

MCP is JSON-RPC, not a conventional REST API. Opening /mcp in the browser address bar will therefore not invoke add. You must initialize an MCP session and then call the tool.

Initialize:

curl.exe -i -N http://127.0.0.1:8000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  --data "{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2025-11-25\",\"capabilities\":{},\"clientInfo\":{\"name\":\"curl\",\"version\":\"1.0\"}}}"

Copy the value of the Mcp-Session-Id response header. Then call add:

curl.exe -N http://127.0.0.1:8000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "Mcp-Session-Id: PASTE_SESSION_ID_HERE" \
  --data "{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools/call\",\"params\":{\"name\":\"add\",\"arguments\":{\"a\":17,\"b\":25}}}"

For browser-based interactive testing, the Inspector you already have is easier than manually managing the JSON-RPC session. The HTTP endpoint is primarily intended for MCP clients such as ChatGPT Desktop, Codex, or the Inspector—not direct browser navigation.