GoogleCloudPlatformAPI

Lightweight helpers for common Google Cloud Platform services. The package wraps Google APIs such as BigQuery, Cloud Storage, Analytics, and Ad Manager to reduce boilerplate when building data pipelines or analytics tools.

Installation

pip install GoogleCloudPlatformAPI

Python 3.10 through 3.14 are supported. Runtime dependencies use bounded compatibility ranges, and CI tests both the minimum Python 3.10 dependency set and the newest compatible dependencies.

For development, install the project in editable mode with the tooling extras:

pip install -e '.[dev]'

Run the project checks locally to match CI:

black --check .
pydocstyle GoogleCloudPlatformAPI
pyright GoogleCloudPlatformAPI
pytest -q --cov=GoogleCloudPlatformAPI --cov-report=term-missing --cov-fail-under=90
python -m build
python -m twine check dist/*

Configure authentication with a service account JSON file through GOOGLE_APPLICATION_CREDENTIALS or supply the path to individual helpers.

Stable public API

Core services and package-defined exceptions are available from the package root:

from GoogleCloudPlatformAPI import (
    AuthenticationError,
    BigQuery,
    CloudStorage,
    GoogleCloudPlatformAPIError,
)

Direct module imports remain supported. See docs/public-api.md for naming, exception, and compatibility rules.

AI-native platform surface

The package uses one canonical capability registry for Python, agent, and MCP/Codex integrations. Each operation has a stable name, semantic version, JSON-compatible input and output schemas, permission metadata, safety level, and bounded timeout.

from GoogleCloudPlatformAPI.ai_native import capability_registry, readiness_score

print(capability_registry.schema())
print(readiness_score(capability_registry))

The wheel includes the machine-readable documentation index, Codex skill, and AI platform documentation:

from GoogleCloudPlatformAPI.assets import read_text_resource

print(read_text_resource("llms.txt"))
print(read_text_resource("codex/SKILL.md"))

See docs/ai-native-platform.md, docs/ai-native-scorecard.md, and llms.txt for the repository copies of these contracts.

Local Codex plugin surface

The package includes a read-only MCP server for Codex CLI, the Codex desktop app, and compatible local MCP clients. It runs locally over stdio, inherits the current process environment, and never copies or persists Google credentials.

Install the project and verify the entry point:

pip install 'GoogleCloudPlatformAPI[codex]'
gcp-api-mcp

Register the server in your Codex MCP configuration:

[mcp_servers.google_cloud_platform_api]
command = "gcp-api-mcp"

When using a virtual environment, configure the absolute path to its gcp-api-mcp executable. The server exposes eight read-only tools:

• gcp_context
• bigquery_list_datasets
• bigquery_list_tables
• bigquery_table_schema
• bigquery_query
• gcs_list_objects
• gcs_object_metadata
• gcs_read_text

The intended workflow is discovery first:

BigQuery:      context -> datasets -> tables -> schema -> bounded query
Cloud Storage: context -> objects -> metadata -> bounded text read

BigQuery accepts only statements beginning with SELECT, WITH, or EXPLAIN. Cloud Storage reads and all result sets are bounded. No upload, delete, table creation, or other mutation tool is exposed.

Example Codex prompts:

Show my active GCP context, then list available BigQuery datasets.
Inspect project.dataset.table and draft a safe bounded query.
List JSON objects under reports/ and inspect the newest object's metadata.
Read reports/latest.json, limited to 50000 bytes.

The reusable workflow guidance is stored in .codex/skills/google-cloud-platform-api/SKILL.md and in the installed wheel as GoogleCloudPlatformAPI/assets/codex/SKILL.md.

Usage

BigQuery

from GoogleCloudPlatformAPI import BigQuery

bq = BigQuery()
df = bq.bigquery_to_dataframe("SELECT CURRENT_DATE() AS today")
print(df)

Cloud Storage

from GoogleCloudPlatformAPI import CloudStorage

storage = CloudStorage()
storage.upload_file_from_filename(
    local_file_path="local.txt",
    destination_file_path="data/local.txt",
    bucket_name="my-bucket",
)

Ad Manager

from GoogleCloudPlatformAPI.AdManager import GamClient

# Assumes GOOGLE_APPLICATION_CREDENTIALS is set
gam_client = GamClient()
network_service = gam_client.get_service(
    service_name="NetworkService",
    gam_version="v202602",
)
print(network_service.getCurrentNetwork())

Analytics

from GoogleCloudPlatformAPI import Analytics

# Assumes GOOGLE_APPLICATION_CREDENTIALS is set
analytics = Analytics()
profile_id = "12345678"
report = analytics.get_realtime_report(profile_id)
print(report)

OAuth

from GoogleCloudPlatformAPI import ServiceAccount

# Assumes GOOGLE_APPLICATION_CREDENTIALS is set
creds = ServiceAccount.get_service_account_client()
print(creds.project_id)