Academic Remote MCP Server

A minimal MCP (Model Context Protocol) server, deployed remotely on Google Cloud Run, providing a single tool: random study tips.

Built for CC3067 Redes, Project 1 (Universidad del Valle de Guatemala) — functional requirement #7 (remote MCP server). Per the assignment instructions, this server's functionality is intentionally trivial; what matters is that it runs remotely and is reachable over the network.

Tool exposed

Example

Request:

{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"get_random_study_tip","arguments":{}}}

Response:

{"jsonrpc":"2.0","id":3,"result":{"content":[{"type":"text","text":"Prioriza entender antes de memorizar: lo que comprendes se te olvida menos."}]}}

Related MCP server: Guess Number MCP Server

Architecture

Chatbot (local)
      │
      │ HTTPS (Streamable HTTP, stateless)
      ▼
   Internet
      │
      ▼
Google Cloud Run
      │
      ▼
FastMCP server (get_random_study_tip)

The server runs in stateless HTTP mode (stateless_http=True). This is a deliberate design choice: Cloud Run can scale to multiple container instances, and each instance keeps MCP sessions in its own memory. In stateful mode, a follow-up request routed to a different instance than the one that opened the session would fail with a "session not found" error. Since this server's tool needs no session state at all, running stateless avoids that failure mode entirely.

Requirements

• Python 3.12+

• Docker (for local testing and for building the Cloud Run image)

• A Google Cloud project with the Cloud Run and Cloud Build APIs enabled

Installation (local)

git clone <this-repository-url>
cd academic-remote-mcp

python3 -m venv venv
source venv/bin/activate      # on Windows: venv\Scripts\activate

pip install -r requirements.txt

Running locally

export PORT=8080
python src/server.py

Or with Docker (recommended, matches the production environment exactly):

docker build -t academic-remote-mcp .
docker run -p 8080:8080 -e PORT=8080 academic-remote-mcp

Test the handshake:

curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

Deployment (Google Cloud Run)

gcloud run deploy academic-remote-mcp \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --port 8080

Cloud Run automatically injects the PORT environment variable; the server reads it at startup rather than hardcoding a port.

Deployed endpoint: https://academic-remote-mcp-842046673187.us-central1.run.app/mcp

Connecting from an MCP host

Using the official mcp Python SDK's Streamable HTTP client:

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async with streamablehttp_client(
    "https://academic-remote-mcp-842046673187.us-central1.run.app/mcp"
) as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        result = await session.call_tool("get_random_study_tip", {})

Project Structure

academic-remote-mcp/
├── src/
│   └── server.py
├── Dockerfile
├── requirements.txt
└── README.md

A note on the mcp SDK version

This server uses mcp.server.fastmcp.FastMCP (the mcp 1.x API), pinned via requirements.txt (mcp==1.29.1) to stay consistent with the other repositories in this project. The SDK's 2.x release renamed FastMCP to MCPServer and moved host/port from the constructor to .run(); if upgrading, adjust src/server.py accordingly.

License

MIT.