google-ads-mcp

Production-ready remote MCP server that connects ChatGPT Business (or any MCP client) to the Google Ads API using service-account authentication.

Built for the Myth Nightclub Google Ads account with conservative initial usage (monitoring, reporting, small approved optimizations), while exposing broad generic read/mutate capabilities so you do not need to redesign the connector later.

Stack

• Python 3.12

• FastMCP (HTTP / Streamable MCP)

• Official google-ads Python client (uses the library's current default API version, e.g. v25 with google-ads 32.x)

• Uvicorn ASGI on Railway

Related MCP server: Google Ads Campaign MCP

Endpoints

Environment variables

GOOGLE_ADS_CUSTOMER_ID=
GOOGLE_ADS_LOGIN_CUSTOMER_ID=
GOOGLE_SERVICE_ACCOUNT_JSON=
MCP_API_KEY=
MCP_TOOL_MODE=compact
MAX_BUDGET_CHANGE_PERCENT=15
MAX_BID_CHANGE_PERCENT=15
PORT=

Notes:

• MCP_TOOL_MODE controls how many tools ChatGPT sees at the MCP layer (default compact). All tools remain implemented in code; full exposes every registered tool (~200+).

• GOOGLE_ADS_CUSTOMER_ID must be 10 digits, numbers only (no hyphens). Hyphens in input are stripped automatically.

• GOOGLE_ADS_LOGIN_CUSTOMER_ID is optional (use for MCC / manager access).

• GOOGLE_SERVICE_ACCOUNT_JSON is the full service account JSON as one variable. Escaped \n inside private_key is supported.

• Developer token is not required for this deployment model (Google Cloud project API access level controls production access).

• Secrets are never logged.

Local development

python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
export GOOGLE_ADS_CUSTOMER_ID=XXXXXXXXXX
export GOOGLE_SERVICE_ACCOUNT_JSON='{"type":"service_account",...}'
export MCP_API_KEY=your-secret
uvicorn app.server:app --host 0.0.0.0 --port 8000

Run tests:

pytest

Railway deployment

1. Create a new Railway project from this repository.

2. Set Root Directory to repo root (contains Dockerfile).

3. Add environment variables from .env.example (paste service account JSON into GOOGLE_SERVICE_ACCOUNT_JSON).

4. Railway sets PORT automatically; the container listens on 0.0.0.0:$PORT.

5. Deploy and verify:

   • GET https://<your-app>.up.railway.app/health

6. Configure ChatGPT Business MCP:

   • URL: https://<your-app>.up.railway.app/mcp

   • Header: Authorization: Bearer <MCP_API_KEY>

Railway checklist

• Service account has Standard access on the Google Ads account

• Google Cloud project has Google Ads API enabled with appropriate access level

• GOOGLE_ADS_CUSTOMER_ID set (Myth Nightclub account, digits only)

• MCP_API_KEY set to a long random secret

• /health returns {"status":"ok",...}

MCP tool exposure modes

GET /health includes mcp_tool_mode, tools_exposed, and tools_implemented.

Safety controls

All mutation tools support:

• dry_run: bool — uses validate_only when the API supports it

• override_limits: bool — bypasses convenience budget/bid guardrails

Environment guardrails (convenience tools):

• MAX_BUDGET_CHANGE_PERCENT

• MAX_BID_CHANGE_PERCENT

Generic raw mutation tools (google_ads_mutate, google_ads_service_call, etc.) emit a limit warning when guardrails are configured but not overridden.

Remove operations always return an explicit remove summary (resource name + customer ID).

Architecture

app/
  server.py            # FastMCP + /health + ASGI app
  auth.py              # Service account JSON parsing
  config.py            # Environment settings
  google_ads_client.py # Client factory + GAQL execution
  gaql.py              # GAQL validation
  errors.py            # Google Ads error formatting
  audit.py             # Structured stdout audit logs
  safety.py            # Budget/bid limits
  mutations.py         # Generic mutate + service call dispatch
  services_registry.py # Dynamic service/method allowlist
tools/
  raw_api.py           # Generic/future-proof tools
  reporting.py         # GAQL convenience reads
  campaigns.py ...     # Domain write tools

Tool surface (summary)

Generic / future-proof

• google_ads_query

• google_ads_describe_fields

• google_ads_list_services

• google_ads_service_call

• google_ads_mutate

• google_ads_batch_mutate / google_ads_multi_mutate

• google_ads_resource_mutate

• google_ads_batch_job

Convenience reads (GAQL)

Account, campaigns, budgets, ad groups, ads, keywords, search terms, segments (geo/device/network/time), assets & extensions, conversions, recommendations, policy, bidding, billing, experiments, audiences, Performance Max, shopping/video/display/search campaign filters, negatives, labels, geo/language constants, etc. (see tools/reporting.py).

Convenience writes

Campaigns, budgets, bidding, ad groups, keywords/negatives, targeting, ads (RSA), assets, conversions upload, recommendations, audiences/user lists, Performance Max, experiments, labels, billing setup, batch jobs.

Use google_ads_list_services + google_ads_service_call for any additional Google Ads service methods not wrapped by a named convenience tool.

Known API Coverage Gaps

• Keyword Plan Idea / historical metrics: use google_ads_service_call with KeywordPlanIdeaService (not full GAQL).

• Customer Match member removal: requires OfflineUserDataJobService job workflow; helper returns guidance.

• Invoices: invoice is not a GoogleAdsService GAQL resource in API v25. get_invoices_if_supported returns guidance; use InvoiceService.list_invoices via google_ads_service_call (billing setup + issue month; may require elevated access).

• conversion_goal resource: removed. Convenience reads use custom_conversion_goal (plus campaign_conversion_goal / customer_conversion_goal). The newer goal resource is not wrapped as a named convenience tool.

• campaign_experiment resource: removed. Use experiment and experiment_arm.

• Combined age + gender demographics: not supported in one GAQL query. get_demographic_performance uses gender_view only; query age_range_view separately via google_ads_query.

• Asset / listing metrics: some FROM asset metric combinations can be rejected by the live API even when fields exist on the proto. Errors are returned verbatim — do not assume every convenience report is selectable together.

• Per-service named wrappers: all installed services are reachable via google_ads_service_call; not every service has a dedicated convenience alias.

• Explorer access limitations: write or specialized endpoints may fail until Google Cloud API access is upgraded — errors include suggested actions.

• Resource mutate dispatcher: maps to the primary mutate_* method per service; exotic operation shapes may require google_ads_service_call with an explicit request payload.

Audit logging

Structured JSON logs for write operations go to stdout (Railway log retention), including tool name, customer ID, dry-run flag, resource names, success/failure, and Google request ID when available. Secrets are not logged.