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 Boilerplate deploy a remote MCP server to Google Cloud with authentication"
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.
This repository contains the code to demonstrate MCP capabilities
–2. Create a virtual environment and setup dependencies
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txtRelated MCP server: Google Cloud MCP Server
We're going to deploy a remote MCP server to google cloud which also supports authentication. Navigate to the server remote-mcp-gcp directory and perform the following steps
Install uv on your computer if you'd like to run the invoke the MCP server locally using your testing script.
Rename .envrc_sample to .envrc so that your shell can pickup the GCLOUD_PROJECT_ID environment variable mv .envrc_sample .envrc
Update your GCLOUD_PROJECT_ID in your .envrc file with the ID of your google cloud project.
Create a container registry to host your MCP server container image
gcloud artifacts repositories create remote-mcp-servers \
--repository-format=docker \
--location=us-central1 \
--description="Repository for remote MCP servers" \
--project=$GCLOUD_PROJECT_IDSubmit a build job for the container image. We'll use remote build for this.
gcloud builds submit --region=us-central1 --tag us-central1-docker.pkg.dev/$GCLOUD_PROJECT_ID/remote-mcp-servers/mcp-server:latestCreate a container cloud run instance with
gcloud run deploy mcp-server \
--image us-central1-docker.pkg.dev/$GCLOUD_PROJECT_ID/remote-mcp-servers/mcp-server:latest \
--region=us-central1 \
--no-allow-unauthenticatedCreate a proxy to invoke the remote endpoint from your computer with authentication. gcloud run services proxy mcp-server --region=us-central1. This will ask you to install cloud run proxy tooling on your computer.
Now, localhost:8080 should be pointed to your deployed instance with authentication enabled
Run uv run test_server.py to invoke the client script against the remote server with various tools.
Feel free to create new tools and experiment.
Once you're done, delete all associated resources from Google Cloud to avoid unnecessary charges.
FastMCP vs MCP Python SDK.Read this issue for more info but generally FastMCP is much more preferred by developers and you'll be able to build much more capabilties with the same.
This server cannot be deployed
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
A Model Context Protocol server for Wix AI tools
The Google Compute Engine MCP server is a fully-managed Model Context Protocol server that provides tools to manage Google Compute Engine resources through AI agents. It enables capabilities including instance management (creating, starting, stopping, resetting, listing), disk management, handling instance templates and group managers, viewing machine and accelerator types, managing images, and accessing reservation and commitment information. The server operates as a zero-deployment, enterprise-grade endpoint at https://compute.googleapis.com/mcp with built-in IAM-based security.
Remote MCP for Gemini upgrade evals, prompt regressions, output diffs, and eval receipts.
