Gadschain

io.github.SnipMCPPublished by GitHub account SnipMCPv0.1.1Updated Oct 27, 2026

Google Ads MCP server. Read campaigns, manage budgets, pause/enable, add negative keywords.

VerifiedSTDIODesktop onlyData & AnalyticsBusiness & Commerce

Overview

AI-generated overview

Local MCP server that lets an assistant read and adjust Google Ads campaigns: budgets, pause/enable status, and negative keywords.

What you can do
  • List campaigns and review search terms with spend and conversion metrics
  • Change a campaign's daily budget in dollars
  • Pause or enable campaigns
  • Add negative keywords to a campaign with a chosen match type
  • Return structured, sanitized results with readable errors instead of raw API payloads
When to use it
Useful for day-to-day Google Ads account operations: spotting campaigns or search terms that waste spend, adjusting budgets, pausing or resuming campaigns, and adding negative keywords, all from a chat session.
Requirements
Runs locally as a Python package (pip install, or Docker). Needs Google Ads API credentials supplied through environment variables: GOOGLE_ADS_DEVELOPER_TOKEN, GOOGLE_ADS_CLIENT_ID, GOOGLE_ADS_CLIENT_SECRET, GOOGLE_ADS_REFRESH_TOKEN, plus GOOGLE_ADS_LOGIN_CUSTOMER_ID and GOOGLE_ADS_CUSTOMER_ID. Network access to the Google Ads API.
Before you install
Requires a developer token and OAuth client credentials plus a refresh token, which grant access to a live ad account; keep them out of logs and shared configs. Budget changes, pause/enable and negative keywords write to the real account and can affect spending. The README also promotes a paid managed cloud offering.

Installation

In SourceWeft

  1. Open Gadschain in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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.

[GadsChain Demo]

☁️ 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

bash
git clone https://github.com/SnipMCP/gadschain.gitcd gadschainpip install -e ".[dev]"cp .env.example .env

Or with Docker:

bash
docker-compose up --build

Configuration

env
GOOGLE_ADS_DEVELOPER_TOKEN=your_developer_token_hereGOOGLE_ADS_CLIENT_ID=your_oauth_client_id_hereGOOGLE_ADS_CLIENT_SECRET=your_oauth_client_secret_hereGOOGLE_ADS_REFRESH_TOKEN=your_refresh_token_hereGOOGLE_ADS_LOGIN_CUSTOMER_ID=1234567890   # MCC (manager), digits onlyGOOGLE_ADS_CUSTOMER_ID=1234567890         # default operating accountGOOGLE_ADS_API_VERSION=v24LOG_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

bash
# Tab 1 — start the MCP serverpython -m gadschain.server
bash
# 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):      $51.41Clicks:         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.

Source: README.md at commit c95819c

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.1LatestSep 16, 2026

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