GCP Platform Engineer

Name: GCP Platform Engineer
Author: deBilla

by deBillaversion 0.4.0i★ –iRuns on your computer

Registry nameio.github.deBilla/platform-mcp

Install

Overview

Description from the maker (original English)

Read-only GCP tools for debugging incidents and finding cost savings

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: PLATFORM_MCP_ENVIRONMENTS, PLATFORM_MCP_DEFAULT_ENVIRONMENT, PLATFORM_MCP_CONFIG, PLATFORM_MCP_AUDIT_LOG

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "platform": {
      "command": "uvx",
      "args": [
        "platform-mcp"
      ],
      "env": {
        "PLATFORM_MCP_ENVIRONMENTS": "your-value",
        "PLATFORM_MCP_DEFAULT_ENVIRONMENT": "your-value",
        "PLATFORM_MCP_CONFIG": "your-value",
        "PLATFORM_MCP_AUDIT_LOG": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add platform --env PLATFORM_MCP_ENVIRONMENTS=your-value --env PLATFORM_MCP_DEFAULT_ENVIRONMENT=your-value --env PLATFORM_MCP_CONFIG=your-value --env PLATFORM_MCP_AUDIT_LOG=your-value -- uvx platform-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "platform": {
      "command": "uvx",
      "args": [
        "platform-mcp"
      ],
      "env": {
        "PLATFORM_MCP_ENVIRONMENTS": "your-value",
        "PLATFORM_MCP_DEFAULT_ENVIRONMENT": "your-value",
        "PLATFORM_MCP_CONFIG": "your-value",
        "PLATFORM_MCP_AUDIT_LOG": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "platform": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "platform-mcp"
      ],
      "env": {
        "PLATFORM_MCP_ENVIRONMENTS": "your-value",
        "PLATFORM_MCP_DEFAULT_ENVIRONMENT": "your-value",
        "PLATFORM_MCP_CONFIG": "your-value",
        "PLATFORM_MCP_AUDIT_LOG": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

PLATFORM_MCP_ENVIRONMENTS env
JSON map of environment name to GCP settings. Omit when using a config file at ~/.config/platform-mcp/config.toml.
PLATFORM_MCP_DEFAULT_ENVIRONMENT env
Environment used when a tool call omits the environment argument.
PLATFORM_MCP_CONFIG env
Path to a TOML config file, overriding ~/.config/platform-mcp/config.toml.
PLATFORM_MCP_AUDIT_LOG env
Path for the JSONL tool-call audit log, or 'off' to disable it.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account deBilla, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 1392 servers in this category
Published to the registry more recently than 47% of the other 1392 servers in this category
Basic checks equal to or better than 100% of the other 1392 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is GCP Platform Engineer safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

platform-mcp

mcp-name: io.github.deBilla/platform-mcp

A read-only Model Context Protocol server that turns an AI agent (Claude Code, Claude Desktop, or any MCP client) into a GCP platform engineer. Point it at your Google Cloud projects and ask it to investigate incidents, take inventory, and surface cost-optimization opportunities — all without any ability to change your infrastructure.

Observation only. No tool in this server mutates state. Combined with a viewer-only identity (below), that gives you a hard, defense-in-depth guarantee that an agent can look but never touch.

What it can do

Area	Tools
Environments	`list_environments`
Logs & errors	`query_logs`, `get_recent_errors`, `list_error_groups`
Metrics & alerting	`query_metric`, `list_alert_policies`, `list_uptime_checks`
Cost & recommendations	`get_cost_breakdown`, `get_billing_info`, `list_cost_recommendations`, `list_recommendations`
Resource inventory	`search_assets`, `list_compute_instances`, `list_cloud_run_services`, `list_gke_clusters`, `list_sql_instances`

Typical prompts once it's connected:

*"What are the top error groups in the last 24 hours, and which one is newest?"*
*"Which GKE node pools are over-provisioned? Show mean CPU against machine type."*
*"Where can I reduce spend in this project?"*

Multiple environments

One server can reach several projects. Define them under PLATFORM_MCP_ENVIRONMENTS (see Configuration) and the agent picks one from the wording of your prompt:

*"Any errors in staging in the last hour?"*
*"Compare Cloud Run services between staging and prod."*

Every tool takes an optional environment argument. Omit it and the default environment is used; pass environment="production" to target another. Names, any aliases you define, common shorthands (prod, stg, qa, …) and bare project ids all resolve. An unrecognized name is an error listing the valid options — a typo can never silently retarget the wrong project.

Each environment carries its own service account, so staging and production are reached through separate identities from the same process, and every result echoes back the environment and project it came from.

Requirements

Python 3.11+
A Google Cloud project and credentials (your own login, or a service account)
The gcloud CLI for the one-time setup

Install

uvx platform-mcp          # no install step; uv fetches it on demand

pipx install platform-mcp # or keep it on PATH

From a checkout, for development:

git clone https://github.com/deBilla/platform-mcp.git

cd platform-mcp python3 -m venv .venv ./.venv/bin/pip install -e ".[dev]"

Check your setup

platform-mcp doctor

This checks, for every configured environment, that Application Default Credentials exist, that the read-only service account can be impersonated, that a real API read succeeds, and that the billing export is readable — printing the exact command to fix whatever fails. Run it before reporting a problem.

One-time GCP setup

Run these once per project you want to reach — staging and production each need their own APIs enabled and their own read-only service account.

1. Enable the APIs the tools depend on:

gcloud services enable \

logging.googleapis.com monitoring.googleapis.com clouderrorreporting.googleapis.com \ recommender.googleapis.com cloudasset.googleapis.com cloudbilling.googleapis.com \ bigquery.googleapis.com \ --project YOUR_PROJECT_ID

2. Grant read-only access to the identity the server runs as.

For local development with your own login (Application Default Credentials):

gcloud auth application-default login

The identity needs these viewer roles on the project, plus roles/billing.viewer on the billing account:

roles/viewer                # broad read (compute, run, gke, sql via Asset Inventory)

roles/logging.viewer roles/monitoring.viewer roles/errorreporting.viewer roles/recommender.viewer roles/cloudasset.viewer roles/bigquery.dataViewer # only for get_cost_breakdown roles/bigquery.jobUser # only for get_cost_breakdown

3. (Recommended) Use a dedicated read-only service account instead of your login. platform-mcp setup does every step below, is safe to re-run, and prints the config stanza at the end. It ships with the package, so there is nothing to clone:

uvx platform-mcp setup \

--project YOUR_PROJECT_ID \ --user you@example.com \ --billing-dataset YOUR_BILLING_PROJECT:billing # optional

Or by hand:

PROJECT=YOUR_PROJECT_ID

gcloud iam service-accounts create platform-mcp-ro \ --display-name "platform-mcp read-only" --project $PROJECT

SA=platform-mcp-ro@$PROJECT.iam.gserviceaccount.com for ROLE in roles/viewer roles/logging.viewer roles/monitoring.viewer \ roles/errorreporting.viewer roles/recommender.viewer roles/cloudasset.viewer \ roles/bigquery.jobUser; do gcloud projects add-iam-policy-binding $PROJECT \ --member="serviceAccount:$SA" --role="$ROLE" --condition=None done

Let your own login impersonate it (no key file to manage):

gcloud iam service-accounts add-iam-policy-binding $SA \ --member="user:you@example.com" \ --role="roles/iam.serviceAccountTokenCreator" --project $PROJECT

The grant everyone forgets. roles/bigquery.jobUser above only lets the account *start* a query; it grants no access to any data. A billing export almost always lives in a different project, so the account also needs read on that dataset. Without it get_cost_breakdown returns 403 while every other tool works, which reads like a bug in the tool rather than a missing grant:

bq add-iam-policy-binding \

--member="serviceAccount:$SA" --role=roles/bigquery.dataViewer \ YOUR_BILLING_PROJECT:billing

If you lack admin on the billing project, that one line is what to send to someone who has it. platform-mcp doctor checks it and says which side is missing.

Then reference it as that environment's impersonate value in PLATFORM_MCP_ENVIRONMENTS (preferred — no key file), or point at a downloaded key via GOOGLE_APPLICATION_CREDENTIALS.

Impersonation is performed by whatever identity your ADC resolves to. If your

ADC is itself an impersonated service account, that SA — not your user — needs

roles/iam.serviceAccountTokenCreator on each platform-mcp-ro.

Security model

Read-only is enforced by IAM, not by OAuth scope. The server requests the broad cloud-platform scope and stays read-only purely because it never calls a mutating API. Do not rely on the code alone — run it under a viewer-only identity (step 3 above) so the credential itself is incapable of writing, regardless of what code executes. This gives you two independent layers: the server doesn't try to write, and the identity couldn't if it did.

With multiple environments this stays per-project: each environment authenticates as its own service account, so a staging identity is never used to reach production. Grant each one viewer-only access to its project alone.

Configuration

The friendliest option is a config file, which keeps project ids and service account emails out of every client config you own:

mkdir -p ~/.config/platform-mcp

cp config.toml.example ~/.config/platform-mcp/config.toml $EDITOR ~/.config/platform-mcp/config.toml

With that in place, registering the server takes no environment variables at all. Point PLATFORM_MCP_CONFIG elsewhere to use a different file — a copy committed to your infrastructure repo, for instance.

Environment variables still work and always win over the file, so an existing setup keeps running unchanged and a one-off override needs no edit:

Variable	Purpose
`PLATFORM_MCP_ENVIRONMENTS`	JSON map of environment name → settings. The recommended way to configure the server.
`PLATFORM_MCP_DEFAULT_ENVIRONMENT`	Environment used when a tool call omits `environment`. Defaults to `staging` if configured, else the first entry.
`GOOGLE_APPLICATION_CREDENTIALS`	Path to a read-only SA key file (alternative to impersonation).

PLATFORM_MCP_ENVIRONMENTS holds a JSON object; each entry accepts:

Key	Purpose
`project`	Required. GCP project id.
`impersonate`	Read-only SA to impersonate for this environment (no key file needed).
`billing_export_table`	Fully-qualified BigQuery billing export table, required only for `get_cost_breakdown` (e.g. `YOUR_PROJECT_ID.billing.gcp_billing_export_v1_XXXXXX`).
`aliases`	Extra names the agent may use for this environment.

A bare string value is shorthand for {"project": "..."}. As JSON inside .mcp.json the quotes must be escaped; unescaped it reads:

"staging": { "project": "my-app-staging", "impersonate": "platform-mcp-ro@my-app-staging.iam.gserviceaccount.com" }, "production": { "project": "my-app", "impersonate": "platform-mcp-ro@my-app.iam.gserviceaccount.com", "billing_export_table": "my-app.billing.gcp_billing_export_v1_XXXXXX" } }

Single-environment mode. If PLATFORM_MCP_ENVIRONMENTS is unset the server behaves as before, exposing one environment named default:

Variable	Purpose
`GCP_PROJECT`	Target project. Falls back to your ADC default project if unset.
`IMPERSONATE_SERVICE_ACCOUNT`	Read-only SA to impersonate. Also the fallback for registry entries with no `impersonate`.
`BILLING_EXPORT_TABLE`	Billing export table. Also the fallback for registry entries with no `billing_export_table`.

Register with a client

Claude Code — with a config file in place, this is the whole thing:

claude mcp add platform-mcp --scope user -- uvx platform-mcp

Claude Desktop — needs an absolute path to uvx; see Claude Desktop for the full walkthrough.

Without a config file, add the environment variables from .mcp.json.example to either form.

Skip the approval prompt

Every tool here is read-only, so approving each call individually adds nothing. Allow the whole server once, in Claude Code settings:

{ "permissions": { "allow": ["mcp__platform-mcp__*"] } }

The glob must sit after a literal mcp__ __ prefix — an unanchored pattern like mcp__* is ignored with a warning and approves nothing.

MCP Inspector — for interactive testing:

uvx --with 'mcp[cli]' mcp dev src/platform_mcp/server.py

Claude Desktop

Installing for a teammate who uses Claude Desktop rather than the CLI. It works the same as Claude Code, with one failure mode the CLI does not have.

Claude Desktop does not inherit your shell `PATH`. It launches from the Finder, so uvx and anything installed by Homebrew or uv are invisible to it. A config that says "command": "uvx" fails with ENOENT — the server never starts, and the error names the command rather than the reason. The command path must be absolute.

What does *not* break is credentials. Application Default Credentials are a file the Google libraries read directly, so gcloud is needed once, in a terminal, to create that file — not at runtime. The config file is found the same way, from the home directory, whatever launched the server.

Before you start: the admin grant

Each person impersonates the read-only service accounts, so an admin runs this once per person, per environment (the platform-mcp setup script does it for whoever runs it, but not for anyone else):

gcloud iam service-accounts add-iam-policy-binding \

platform-mcp-ro@YOUR_PROJECT_ID.iam.gserviceaccount.com \ --member="user:teammate@example.com" \ --role="roles/iam.ser

Only the beginning is shown; read the rest in the repo

GCP Platform Engineer

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

platform-mcp

What it can do

Multiple environments

Requirements

Install

Check your setup

One-time GCP setup

Let your own login impersonate it (no key file to manage):

Security model

Configuration

Register with a client

Skip the approval prompt

Claude Desktop

Before you start: the admin grant