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Gadschain

by SnipMCPversion 0.1.1i★ –iRuns on your computer
Registry nameio.github.SnipMCP/gadschain

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Marketing & social media

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "gadschain": {
      "command": "uvx",
      "args": [
        "gadschain"
      ]
    }
  }
}

Run this in your terminal

claude mcp add gadschain -- uvx gadschain

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "gadschain": {
      "command": "uvx",
      "args": [
        "gadschain"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "gadschain": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "gadschain"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account SnipMCP, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Marketing & social mediai

  • More stars than 0% of the other 3563 servers in this category
  • Published to the registry more recently than 10% of the other 3563 servers in this category
  • Basic checks equal to or better than 100% of the other 3563 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Gadschain safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

An MCP server retrieving transcripts of YouTube videos

Runs on your computer★ 495MITUpdated 10 days ago
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GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.

Runs on your computer★ 244MITUpdated 10 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

Ask PopNx