gcp-mcp-server

Model Context Protocol server for Google Cloud Platform. Built on the MCP Python SDK (mcp>=1.27) using FastMCP, with tools that touch Resource Manager, Compute Engine, Cloud Storage, BigQuery, Pub/Sub, and Cloud Logging.

Features

• Application Default Credentials (ADC) or explicit service-account JSON auth

• Lazy-constructed Google Cloud SDK clients bound to resolved credentials

• Blocking SDK calls executed in worker threads via anyio.to_thread.run_sync

• Typed tools covering the most common GCP day-to-day operations

• Unit tests that mock the SDK clients end-to-end (no real API calls)

Related MCP server: GCP MCP

Install

git clone https://github.com/asklokesh/gcp-mcp-server.git
cd gcp-mcp-server
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Requires Python 3.10 or newer.

Authentication

The server resolves credentials in the following order:

1. If GCP_CREDENTIALS_PATH points to a service-account JSON file, that file is used.

2. Otherwise, Application Default Credentials (ADC) are loaded via google.auth.default().

For ADC, the simplest path is:

gcloud auth application-default login

Or point GOOGLE_APPLICATION_CREDENTIALS at a service-account JSON file.

Environment variables

All settings are optional and use the GCP_ prefix:

Claude Desktop configuration

Add an entry like this to your Claude Desktop mcpServers config:

{
  "mcpServers": {
    "gcp": {
      "command": "gcp-mcp",
      "env": {
        "GCP_DEFAULT_PROJECT_ID": "my-gcp-project",
        "GCP_CREDENTIALS_PATH": "/absolute/path/to/service-account.json"
      }
    }
  }
}

If you prefer ADC, omit GCP_CREDENTIALS_PATH and ensure gcloud auth application-default login was run as the same user that launches Claude Desktop.

Tools

Resource Manager / IAM

• list_projects() - list visible GCP projects

• get_project(project_id) - fetch project metadata

• list_service_accounts(project_id) - list IAM service accounts via the IAM REST API

Compute Engine

• list_instances(project_id?, zone?) - zonal or aggregated instance list

• get_instance(project_id, zone, name) - single instance details

• start_instance(project_id, zone, name) - start a VM

• stop_instance(project_id, zone, name) - stop a VM

Cloud Storage

• list_buckets(project_id?) - list buckets in a project

• list_objects(bucket, prefix?, max_results=100) - list blobs

• get_object_metadata(bucket, name) - fetch blob metadata

BigQuery

• list_datasets(project_id?) - list datasets

• list_tables(project_id, dataset) - list tables

• query_bigquery(project_id, sql, dry_run=False) - run a query; dry-run returns schema and estimated bytes

Pub/Sub

• list_topics(project_id?) - list topics

• list_subscriptions(project_id?) - list subscriptions

• publish_message(project_id, topic, data, attributes?) - publish a message. data is UTF-8; prefix with base64: for binary payloads.

Cloud Logging

• read_logs(project_id, filter, max_entries=50) - advanced log filter, e.g. resource.type="gce_instance"

Required IAM roles

The caller's principal needs appropriate roles per surface. Minimal recommended roles:

Grant only what the caller actually needs.

Development

Run the test suite:

source .venv/bin/activate
pytest -x --tb=short

Lint:

ruff check src/ tests/

Tests mock each Google Cloud client with unittest.mock, so no real GCP APIs are called.

Project layout

src/gcp_mcp/
  __init__.py       # main entry point
  auth.py           # ADC / service-account credential resolution
  clients.py        # lazy SDK client construction
  config.py         # pydantic-settings config (GCP_ env prefix)
  exceptions.py     # GCPError, AuthenticationError, APIError, NotFoundError
  server.py         # FastMCP server + @mcp.tool() definitions
tests/              # unit tests with mocked SDK clients
pyproject.toml
requirements.txt

License

MIT.