Gadschain

GadsChain

The AI layer between your Google Ads account and your marketing decisions.

Battle-tested. Six tools cover the daily-ops loop — campaign listing, search-term review, budget tuning, pause/enable, and negative-keyword grooming. All responses are strict Pydantic models. No raw protobuf reaches the agent.

☁️ Moving to production?

The open-source server runs locally with your own API keys. For hosted infrastructure with multi-account failover, SLA guarantees, and webhook alerts — join the managed cloud waitlist.

The Problem

Raw Google Ads API returns thousands of rows. One bad campaign structure bleeds budget silently. GadsChain reads, sanitizes, and acts on your ad data before waste compounds.

Installation

git clone https://github.com/SnipMCP/gadschain.git

cd gadschain pip install -e ".[dev]" cp .env.example .env

Or with Docker:

docker-compose up --build

Configuration

GOOGLE_ADS_DEVELOPER_TOKEN=your_developer_token_here

GOOGLE_ADS_CLIENT_ID=your_oauth_client_id_here GOOGLE_ADS_CLIENT_SECRET=your_oauth_client_secret_here GOOGLE_ADS_REFRESH_TOKEN=your_refresh_token_here GOOGLE_ADS_LOGIN_CUSTOMER_ID=1234567890 # MCC (manager), digits only GOOGLE_ADS_CUSTOMER_ID=1234567890 # default operating account GOOGLE_ADS_API_VERSION=v24 LOG_LEVEL=INFO

Usage

Three example prompts to send to Claude (or any MCP-compatible agent):

1. Use get_campaigns to show me which campaigns are bleeding budget this month
2. Run get_search_terms for the last 30 days and tell me which queries are wasting spend
3. Add "free", "cheap", "jobs" as negative keywords to campaign 12345

Run it in two terminals

# Tab 1 — start the MCP server

python -m gadschain.server

# Tab 2 — call a tool from a Python shell or your MCP client

Tool signatures:

get_campaigns(customer_id=None)

get_search_terms(customer_id=None, days=30, campaign_id=None)

update_budget(campaign_id, new_budget_dollars, customer_id=None)

pause_campaign(campaign_id, customer_id=None)

enable_campaign(campaign_id, customer_id=None)

add_negative_keywords(campaign_id, keywords, match_type="BROAD", customer_id=None)

How it works

Three layers between raw Google Ads output and your model:

Google Ads API → [Fetch] → [Transform] → [Act] → MCP Tool → AI Agent

GAQL micros→$ safe queries enum→str mutations CTR→% shared-budget guard

• Fetch: Targeted GAQL queries — only the columns the daily-ops loop actually needs. No SELECT *, no protobuf pagination footguns.
• Transform: Currency micros divided to dollars, CTR scaled to percent, enums to human strings, every nested attribute lookup tolerates missing fields without crashing.
• Act: Mutations route through guard rails — REMOVED blocked on status changes, shared budgets refused (shared_budget_refused), match types validated before any mutate call. The agent never gets an exception; it gets a structured {"error": ..., "message": ...} it can reason about.

Real numbers from a live Franka Pizzeria account (28-day window)

RAW GOOGLE ADS PAYLOAD          GADSCHAIN OUTPUT

───────────────────────────────────────────────── Impressions: 3,389 Spend (28d): $51.41 Clicks: 163 Conversions: 3 ($17.14 each) CTR: 4.81% Conv. rate: 1.84% Cost/click: $0.32 avg Surface: Display Network waste identified on Fridays ($0.11 CPC vs $0.44 avg)

In one read of a real account, GadsChain surfaced $51.41 spent over 28 days for 3 conversions at $17.14 each — a 1.84% conversion rate hidden inside a 4.81% CTR that looks healthy on paper. The Display Network was the silent culprit, with Friday clicks averaging $0.11 CPC vs the $0.44 search-side average — cheap junk traffic inflating CTR while contributing nothing to conversions. The agent saw it because the transformed payload made channel attribution legible instead of buried in protobuf.

Roadmap

• Managed cloud tier (hosted, multi-tenant, webhook alerts)
• Phase 2: ChatGPT REST shim (FastAPI surface over the same six tools)
• Bid-strategy tuning tools (target CPA, target ROAS)
• Anomaly alerts on cost-per-conversion drift

Contributing

PRs welcome. Run pytest before submitting.