gcp

GCP / Vertex AI Skill

Anthropic on Vertex AI — Critical Quirks

These are failure modes discovered through direct use. Follow exactly.

Model IDs

Use no version suffix for the latest model on Vertex:

// ✅ Correct (gets latest)
model: 'claude-sonnet-4-5'

// ❌ Wrong (version suffix not supported on Vertex)
model: 'claude-sonnet-4-6'

SDK Selection

Use @anthropic-ai/vertex-sdk, NOT @google-cloud/vertexai:

npm install @anthropic-ai/vertex-sdk

The @google-cloud/vertexai package uses the wrong publisher path for Claude models and will fail with auth or routing errors.

Publisher path: Claude models live at publishers/anthropic, not publishers/google.

anthropic_beta Header

Must be passed as an array in the request body — NOT as an HTTP header:

// ✅ Correct: array in body
client.messages.create({
  model: 'claude-sonnet-4-5',
  max_tokens: 8000,
  anthropic_beta: ['interleaved-thinking-2025-05-14'],  // ← body, not header
  messages: [...]
});

// ❌ Wrong: HTTP header
headers: { 'anthropic-beta': 'interleaved-thinking-2025-05-14' }

thinking Parameter

{ type: 'adaptive' } is NOT supported on Vertex. Use explicit enabled with a budget:

// ✅ Correct for Vertex
thinking: { type: 'enabled', budget_tokens: 10000 }

// ❌ Not supported on Vertex
thinking: { type: 'adaptive' }

Authentication

Standard GCP application default credentials work:

gcloud auth application-default login
gcloud config set project YOUR_PROJECT_ID

The Vertex SDK picks up ADC automatically. No explicit token management needed for local development.

Secret Manager — Env Var Injection Includes Raw Bytes

When Cloud Run (or any GCP service) injects a Secret Manager secret as an environment variable, the raw bytes are used verbatim — including any trailing newline if the secret was stored with one.

os.environ.get("MY_SECRET") returns "value\n" not "value". This causes silent bugs: API validators that check token length reject the value; string comparisons fail; authentication breaks.

Store secrets without trailing newlines:

# Wrong — echo appends \n
echo "my-token" | gcloud secrets versions add my-secret --data-file=-

# Correct
printf '%s' "my-token" | gcloud secrets versions add my-secret --data-file=-
echo -n "my-token" | gcloud secrets versions add my-secret --data-file=-

Verify: gcloud secrets versions access latest --secret=NAME | wc -c should equal exactly the token length (no +1).

In Python, .strip() secrets from env when used in length-sensitive or comparison contexts:

token = os.environ.get("MY_SECRET", "").strip()

Cloud Run Job Creation — Secret Version Required at Deployment Time

When a google_cloud_run_v2_job Terraform/OpenTofu resource mounts a Secret Manager secret as an env var (value_source.secret_key_ref), GCP validates that the secret version exists at job creation time — not at execution time. If the secret resource exists but has no versions yet (just created by TF, not yet populated), the job creation fails with:

Permission denied on secret: projects/.../secrets/<name>/versions/latest for Revision service account ...

This also fails even if the IAM binding (secretmanager.secretAccessor) was just created in the same tofu apply run — GCP IAM propagation lag means the binding may not have taken effect before the job creation validation runs.

The fix: Omit the secret env var from the TF google_cloud_run_v2_job resource entirely. Manage it outside TF with:

# 1. Populate the secret version first
gcloud secrets versions add <secret-name> --data-file=- <<< "$VALUE"

# 2. Wire the mount onto the job
gcloud run jobs update <job-name> \
  --update-secrets ENV_VAR_NAME=<secret-name>:latest \
  --region <region> --project <project>

Use lifecycle { ignore_changes = [...containers[0].env] } on the Cloud Run Job resource so TF doesn't clobber the secret mount on subsequent applies.

Cloud Monitoring — notification_rate_limit Only for Log-Based Alerts

alert_strategy.notification_rate_limit inside google_monitoring_alert_policy (Terraform / OpenTofu) is only valid for log-based alert policies. Applying it to metric-based policies returns HTTP 400:

Error creating AlertPolicy: googleapi: Error 400: Field alertStrategy.notificationRateLimit
had an invalid value: only log-based alert policies may specify a notification rate limit

Fix: omit the alert_strategy block entirely for metric-based policies (e.g., alerting on Cloud Run completed_execution_count):

# ❌ Wrong for metric-based
resource "google_monitoring_alert_policy" "my_alert" {
  alert_strategy {
    notification_rate_limit {
      period = "3600s"
    }
  }
}

# ✅ Correct — no alert_strategy for metric-based
resource "google_monitoring_alert_policy" "my_alert" {
  display_name = "My Alert"
  combiner     = "OR"
  conditions { ... }
  notification_channels = [...]
}

BigQuery + Vertex AI

When combining BigQuery data with Vertex AI Claude calls, prefer the bigquery CLI skill for queries and pass results as structured context in the Claude API request body.