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

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?"

Related MCP server: k8s-readonly-mcp

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

git clone https://github.com/deBilla/platform-mcp.git
cd platform-mcp
python3 -m venv .venv
./.venv/bin/pip install -e .

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:

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; do
  gcloud projects add-iam-policy-binding $PROJECT \
    --member="serviceAccount:$SA" --role="$ROLE"
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

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

Copy the example config and fill in your values:

cp .mcp.json.example .mcp.json

.mcp.json is git-ignored, so your project ids and service-account emails stay local. Environment variables it (or your shell) can set:

PLATFORM_MCP_ENVIRONMENTS holds a JSON object; each entry accepts:

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:

Register with a client

Claude Code / Claude Desktop — add the block from .mcp.json.example to your MCP config (.mcp.json in a project for Claude Code, or claude_desktop_config.json for Desktop), pointing command at the venv's console script so no global install is needed:

{
  "mcpServers": {
    "platform-mcp": {
      "command": "/absolute/path/to/platform-mcp/.venv/bin/platform-mcp",
      "env": {
        "PLATFORM_MCP_DEFAULT_ENVIRONMENT": "staging",
        "PLATFORM_MCP_ENVIRONMENTS": "{\"staging\":{\"project\":\"YOUR_STAGING_PROJECT_ID\",\"impersonate\":\"platform-mcp-ro@YOUR_STAGING_PROJECT_ID.iam.gserviceaccount.com\"},\"production\":{\"project\":\"YOUR_PROD_PROJECT_ID\",\"impersonate\":\"platform-mcp-ro@YOUR_PROD_PROJECT_ID.iam.gserviceaccount.com\"}}"
      }
    }
  }
}

MCP Inspector — for interactive testing:

./.venv/bin/mcp dev src/platform_mcp/server.py

Notes

• All tools cap result counts and truncate long payloads to stay token-friendly.

• GCP clients are built lazily and cached per environment, so switching between staging and production mid-conversation costs one client construction each.

• Cost recommenders are zonal/regional; list_cost_recommendations auto-discovers the locations where you have resources (via Asset Inventory) and fans out, skipping locations and recommenders that are empty or unavailable.

• get_cost_breakdown uses parameterized BigQuery queries with a whitelisted set of group-by columns.

License

MIT © 2026 Dimuthu Wickramanayake