AdRails

AI agents run Google Ads with guardrails in code: plan, validate, commit, human approvals and a hash-chained audit log. Plus an AEO toolkit and direct-mail tracking. Local, open source.

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Documentation

AdRails (adrails.dev)

Guardrails for AI agents running your ads: an open-source, self-hosted Google Ads MCP server.

Website and docs: https://adrails.dev · MCP Registry: dev.adrails/adrails · plain-text summary for agents: https://adrails.dev/llms.txt

AdRails is a local MCP server for Google Ads. An agent may read campaigns, status and search terms as often as it likes; every write has to land in a reviewed desired-state file on disk, get diffed against live state, and clear caps that live in a policy file no AdRails tool writes. The Python package and module are still named adsops — that is the name you install and run.

Three things it does differently:

Your keys. It runs on your machine against your own Google Cloud project and Google Ads credentials. Nothing is hosted: the only endpoint the package talks to is the Google Ads API, and credentials are read from the sources you configure (a service-account JSON file, a static token, or a secret-manager lookup).
Your caps. Caps live in a policy file that no AdRails tool writes; with require_plan_id on, every commit must quote a fresh, single-use plan_id issued for that exact plan; and a change that adds spend exposure is refused while the scheduled guard's heartbeat is stale.
Pay per call in sats. Optional hosted add-ons, Lightning-first, metered per call in sats — coming soon. Nothing in this release contacts any hosted service.

New in 0.6.1: 78 tools. A local-business suite run from the owner's own Claude: Google Local Services Ads leads, reports, junk-lead feedback and verification readiness (built against the v24 API, not yet run on a live LSA account), a lead book for every channel, a generic 90-day growth plan each business's Claude customises, LocalBusiness schema, NAP checks, service-area page briefs and a reviews engine. One-prompt setup (adsops-mcp setup --install-desktop, adsops-mcp connect for a one-click Google sign-in) and MCP prompts start/morning/weekly. ChatGPT Ads (16 tools) since 0.5.0.

Common questions

What is the safest way to let an AI agent manage Google Ads? Give the agent a write path that cannot exceed limits it doesn't control. With AdRails, every change is a git-committed desired-state file, then adsops_plan (a dry-run diff), then Google's validate-only check, then a commit with a single-use plan ID. Caps live in a policy file no AdRails tool can write, and an hourly guard can only pause. Checklist: Google Ads MCP safety: 7 controls.

Is there an open-source Google Ads MCP server that runs locally? Yes. AdRails is Apache-2.0, runs on your machine over stdio with your own Google Cloud project and keys, and installs with pip install adrails. Nothing is hosted.

How do I connect Claude Code, Cursor or Grok to Google Ads? pip install adrails, then adrails init --dir DIR --customer-id ID --campaign-id N --sa KEY.json and adrails doctor, then claude mcp add adrails -- adrails --config DIR/config.json (Cursor and Grok use the same mcpServers entry). See install guides.

Can I require human approval before an AI agent changes my ad budget? Yes. Set approvals.mode to exposure (budget increases, enabling, end-date extensions) or all. The agent calls adsops_approval_request, a human approves with an HMAC-signed token that expires, and a commit without an approved plan_id is refused.

How do I audit every change an AI agent makes to my ad accounts? Every mutating call, including refused ones, is appended to a SHA-256 hash-chained log with secrets redacted. adsops_audit_verify detects edited, deleted or reordered entries. adsops_rollback rebuilds any earlier committed state, and adsops_drift flags edits made outside AdRails.

How do I check whether Google Ads conversion tracking actually works? adsops_conv_audit scores the account 0-100: no primary goal, auto-tagging off, primary actions with zero conversions, double counting, lead actions counting every click, primary actions whose goal is not biddable, spend without conversions, and smart bidding without enough data.

How can an agent check if ChatGPT, Claude and Perplexity can crawl and cite my site? aeo_site_audit checks robots.txt rules for 18 AI and search crawlers (RFC 9309), llms.txt, the sitemap, noindex and canonical tags, and whether the body text is readable without JavaScript. Free browser version: AI crawler checker.

How do I validate or build an llms.txt file? aeo_llms_txt validates a live /llms.txt against the llmstxt.org format, or builds one from a page list.

How do I track whether AI assistants recommend my business? Your agent asks ChatGPT, Claude or Perplexity your category questions and passes the answers to aeo_visibility_record. It scores mentions, rank against competitors, citations and sentiment, and reports the change since the last run. AdRails makes no LLM calls itself.

How do I track response rates and cost per lead for postcards? mail_codes_assign gives every piece a phone-readable tracking code and a UTM-tagged URL. mail_scans counts real scans from your own server logs (bots and your own test scans removed). mail_response reports rates with 95% intervals, and channel_cac puts direct mail cost per lead next to Google Ads. Free browser tools: postcard QR codes, response calculator, holdout calculator.

Does AdRails support ChatGPT Ads (the OpenAI Ads API)? Not yet. The adapter is in development; it will use the same plan, validate and commit path, with OpenAI's Bulk API validate_only and partial_failure forced off.

Testing

python3 -m adsops --selftest           # 430 unit checks, everything mocked
python3 tests/qa_invariants.py         # 200 random sequences against a simulated Google Ads account
python3 tests/qa_inputs.py             # hostile policy/state/config inputs (NaN, Infinity, bad dates, bad UTF-8, ...)
python3 tests/qa_secrets.py            # canary tokens injected everywhere; none may reach output or logs

The QA suites were written adversarially by a separate agent with no write access to adsops/, and every finding was fixed or documented as intended behaviour.

What AdRails cannot guarantee

Read this before pointing it at a live account.

It is a threshold with automatic pause attempts, not a hard spend ceiling. The guard compares spend with numbers you wrote and issues pause operations. That is containment, not billing control.
Google can deliver up to 2x the average daily budget on a given day. A daily budget is an average, not a maximum, and a pause is an API call that can be rejected or land late.
Reporting is delayed. The spend figures AdRails reads are minutes to hours behind delivery.
The guard only acts when it is scheduled and the machine is awake. A chat-session MCP process is not a monitor: while the machine sleeps no checks run, spend keeps moving, and the heartbeat goes stale.
An agent with shell access as the same user can bypass any local tool. AdRails constrains the tool calls it serves, not the machine underneath. Sandbox the agent if that matters to you.
Wrong negative keywords can suppress good traffic. Negatives are append-only here, so a bad one is also awkward to withdraw.
You are responsible for your account and your spend. Keep platform-side billing limits as the real ceiling.

Quickstart

1. Install.

# from PyPI (https://pypi.org/project/adrails/)
pipx install adrails     # console script: adsops-mcp
# or run without installing:
uvx --from adrails adsops-mcp --config /absolute/path/config.json

No other dependencies. Service-account tokens are signed with your system openssl when google-auth isn't installed (install google-auth if you prefer it).

The core is stdlib-only. Check it offline (everything mocked, no network):

python3 -m adsops --selftest      # or: deploy/smoke_docker.sh

2. Write a config. Every account, credential source and path lives here; paths are ~-expanded at load time.

{
  "tenant": "your-tenant",
  "meter_log": "~/.adsops/meter.jsonl",
  "audit_log": "~/.adsops/audit.jsonl",
  "plan_ledger": "~/.adsops/plans.jsonl",
  "lock_dir": "~/.adsops/locks",
  "guard_heartbeat": "~/.adsops/guard.heartbeat",
  "credentials": {"google_ads": {"type": "service_account",
      "sources": [{"kind": "file", "path": "~/.adsops/sa.json"}]}},
  "google_ads": {"api_version": "v24", "end_field": "end_date_time", "login_customer_id": null},
  "commit_policy": {"require_clean_git": true, "allow_commit": true, "allow_set": true},
  "accounts": [{"key": "main", "platform": "google_ads", "customer_id": "000-000-0000",
      "campaigns": [{"key": "search_core", "state_file": "~/ads/search_core.json",
                     "policy_file": "~/ads/search_core.policy.json"}]}]
}

commit_policy defaults to require_clean_git: true and allow_commit: false, so commits and adsops_set stay off until you turn them on.

3. Write the policy file for the campaign (next section), and commit both files to git so git diff is the change review.

4. Register the server in your MCP client. Diagnostics go to stderr; the server speaks JSON-RPC 2.0 over stdio, one object per line.

{
  "mcpServers": {
    "adrails": {
      "command": "python3",
      "args": ["-m", "adsops", "--config", "/absolute/path/config.json"]
    }
  }
}

5. Schedule the guard. A chat session cannot watch spend, so the pause-only spend check runs from your OS scheduler every 15 minutes. Templates for launchd, systemd and cron live in deploy/; render the placeholders (__PYTHON__, __CONFIG__, __PKG__, __LOGDIR__) and read deploy/GUARD.md before installing:

PY=$(command -v python3); CFG=/absolute/path/config.json
PKG=/absolute/dir/containing/adsops; LOGDIR="$HOME/.adsops/logs"
mkdir -p "$LOGDIR"
sed -e "s|__PYTHON__|$PY|g" -e "s|__CONFIG__|$CFG|g" \
    -e "s|__PKG__|$PKG|g" -e "s|__LOGDIR__|$LOGDIR|g" \
    deploy/launchd/com.adsops.guard.plist.template \
    > ~/Library/LaunchAgents/com.adsops.guard.plist
plutil -lint ~/Library/LaunchAgents/com.adsops.guard.plist
launchctl bootstrap gui/$(id -u) ~/Library/LaunchAgents/com.adsops.guard.plist

The command all three units run is PYTHONPATH=$PKG $PY -m adsops --config $CFG guard-all.

Use it from Grok, Claude Code, Cursor or any MCP client

AdRails is a local stdio server, so every client registers the same command. There is no hosted URL, by design.

pip install adrails

# Claude Code
claude mcp add adrails -- python3 -m adsops --config /absolute/path/config.json
# Grok CLI (it also reads ~/.claude.json, .cursor/mcp.json and .mcp.json)
grok mcp add adrails -- python3 -m adsops --config /absolute/path/config.json

Cursor or a project .mcp.json:

{"mcpServers":{"adrails":{"command":"python3","args":["-m","adsops","--config","/absolute/path/config.json"]}}}

Claude Desktop: install the .mcpb bundle from the latest release. More: https://adrails.dev/docs/mcp-clients/

Tools

Tool	What it does
`adsops_campaigns`	Lists the campaigns this deployment may operate: keys, accounts, state files, and the active commit policy.
`adsops_status`	Live read for one campaign: status, end date, daily budget, spend today and lifetime, negative count, whether caps come from the policy file, and the guard heartbeat's age. When the state file manages URL fields it also returns `urls`: the campaign final URL suffix and, per ad group, its custom parameters, final URL suffix and the final URLs of each enabled ad.
`adsops_terms`	Clicked search terms for the last N days (1–90, default 14): term, matched keyword, clicks, cost.
`adsops_plan`	Pure diff of the desired-state file against live state; writes nothing. Covers budget, status, end date, negatives and the URL fields below. Returns the operations, notes (one `url: ...` line per URL change), `ops_count`, the exposure increases it found, and a single-use `plan_id` with its expiry.
`adsops_apply`	Validate-only unless `commit=true`. A commit needs `commit_policy.allow_commit`, a clean git-tracked state file, a fresh guard heartbeat when the change adds exposure, and the `plan_id` from `adsops_plan` when the policy requires it.
`adsops_set`	Proposes a budget / status / end-date / negatives change with a short `reason`; checks it against policy caps and live state, writes the state file and commits it to git. `dry_run=true` returns a `plan_id` instead of writing; `apply=true` needs that `plan_id` when the policy requires it.
`adsops_guard`	Reads spend and pauses the campaign if the daily or lifetime threshold is crossed. Pause only: it never enables, raises a budget or edits keywords. Validate-only unless `commit=true`.
`adsops_usage`	Metering summary for the tenant: calls, errors, units, ops and median latency per tool.
`adsops_negatives_suggest`	Read-only. Classifies the last N days (1–90, default 30) of search terms (renter, job, HOA/condo, vacation, DIY/software, out-of-area, competitor brand) with deterministic rules and an optional local-model hook you supply, and proposes PHRASE negatives with cost and click evidence. Skips terms and phrases your existing negatives already cover, never suggests words on your protect list, and returns a ready-to-paste `negatives_add` patch plus a basis note. It writes nothing: the patch goes through the desired-state file like any other change.
`adsops_lp_diagnose`	Read-only. Per ad group, Google's quality-score components (expected CTR, ad relevance, landing page experience) plus a plain HTTP check of each final URL: status, response time, bytes, and whether the title and H1 echo the ad group's keywords and town. Returns plain-English findings, worst first. No browser and no JavaScript, so a headline built by script shows as missing.
`adsops_competitors_report`	Read-only. The AdRails Intel weekly report from the free sources (Auction Insights you imported, cached Transparency Center data, your own stats snapshots). Needs `intel_config` in the config; makes no network call.
`adsops_auction_insights_import`	Imports an Auction Insights CSV/TSV export or a pasted table into the local intel database (replaces rows for the same date range). Writes only that local SQLite file. Needs `intel_config`.

New in 0.4.0: enterprise controls, automation and an AEO toolkit (28 tools)

Tool	What it does
`adsops_conv_audit`	Read-only. Audit conversion tracking for the campaign's account: primary goals, dead tags, double counting, auto-tagging, lead counting, smart bidding without enough conversions. Returns a 0-100 score, grade, and ranked findings with fixes. Run this before trusting any CPA or optimising toward conversions.
`adsops_diagnostics`	Read-only PPC diagnostics bundle for one campaign: wasted spend (zero-conversion search terms), impression share lost to budget vs rank, cost-weighted quality score with weak components, and day-over-baseline anomalies (z-score). Returns a ranked headline list plus details.
`adsops_rsa_validate`	Pure check, no account access. Validate a Responsive Search Ad draft against Google's limits and common disapproval causes (lengths incl. double-width chars, pins, duplicates, caps, punctuation, phone numbers, unsubstantiated superlatives, banned phrases) before any write. Returns errors, warnings and fixes.
`adsops_rules_check`	Read-only. Evaluate the automation rules in the campaign's POLICY file (founder-owned, agents can't edit it) against live metrics. Returns PROPOSALS and a desired-state patch; nothing is applied. Rules can only pause, add negatives, alert, or move budget inside the policy caps.
`adsops_monitor_check`	Read-only. Run monitors (CPA spike, pacing over/under, month forecast, zero impressions, disapprovals, budget-limited) over live snapshots of every configured campaign (or one). New alerts are de-duplicated for 24 h; with deliver=true they are posted to the webhook named in config alerts.webhook_env.
`adsops_brief`	Read-only. A short markdown brief across every configured campaign: spend today, conversions, blended CPA and the top issues from the monitors. Built for a daily Slack/email post.
`adsops_fleet`	Read-only. Fleet view across every configured account and campaign: spend, budget, conversions and CPA by account and platform, plus the AGGREGATE cap check (config fleet_caps), which assumes platforms may deliver up to 2x the daily budget.
`adsops_realloc_suggest`	Read-only. Recommend moving daily budget from weak campaigns to budget-limited strong ones inside each policy's caps. The total never rises. Returns moves plus per-campaign desired-state patches to review.
`adsops_drift`	Read-only. Who changed the account outside AdRails? Reads Google's change history for the last N days (max 30) and flags out-of-band edits (UI, scripts, auto-applied recommendations), highlighting budget, status and bidding changes.
`adsops_keyword_ideas`	Read-only. Keyword Planner ideas (searches, competition, top-of-page bid range) for seed keywords and/or a URL, clustered by theme with an intent label. Needs a Basic-access developer token.
`adsops_offline_conv_plan`	Build (never send) an offline / enhanced conversion upload from CRM rows. Emails, phones and names are normalised and SHA-256 hashed LOCALLY, so raw PII never leaves this machine. Returns a redacted preview and counts; validate_only stays true.
`adsops_history`	Read-only. Git history of the campaign's desired-state file: every change agents or lanes made, newest first.
`adsops_rollback`	Undo: build the desired state as of an earlier commit (guards, account and campaign ids are never reverted, and negatives are never removed). With commit=true it writes and git-commits the state file; then run adsops_plan and adsops_apply as usual. Nothing reaches the ad account from this tool.
`adsops_approval_request`	Ask a human to approve a plan before it can be committed (when config approvals.mode is on). Returns approve/reject tokens to send to the approver; the tokens are HMAC-signed and expire.
`adsops_approval_decide`	Record a human decision using a signed approve/reject token. Forged, expired or reused tokens are refused.
`adsops_approvals`	List approval requests (newest first), optionally by status.
`adsops_audit_verify`	Verify the tamper-evident audit chain of every mutating call (hash-linked entries). Returns ok, entry count, the first bad entry if any, and a head digest to publish so later truncation is detectable.
`adsops_audit_tail`	The last N audit-chain entries (redacted), newest first.
`adsops_sbom`	Supply-chain manifest of this AdRails install: a CycloneDX 1.5 SBOM and a SHA-256 manifest of every package file, so you can verify the code you run is the code that was released.
`adsops_whoami`	This server's identity and controls: version, principal and roles (RBAC), approval mode, audit chain path, and whether a guard heartbeat is configured.
`aeo_site_audit`	Read-only. Can AI answer engines reach this site? Checks robots.txt rules for 18 AI and search crawlers (GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-User, PerplexityBot, Google-Extended, Bingbot, Applebot...), llms.txt, the sitemap, and the homepage's noindex/canonical/body-text signals. Returns a 0-100 score and ranked fixes.
`aeo_page_audit`	Read-only. Answer-readiness of one page: answer-first paragraph, question headings, FAQ, structured data, specifics, freshness, citations, scannability, plus a JSON-LD audit (required/recommended fields per type) and crawler directives. Returns scores, extractable answer blocks, and missing questions.
`aeo_llms_txt`	Read-only. Validate a site's /llms.txt (llmstxt.org format), or build one: pass pages [{url,title,description,section}] with title and summary and it returns a ready file that passes validation.
`aeo_schema_generate`	Pure. Generate valid JSON-LD for a page: kind = faq (qas [[q,a]...]), software (name,url,description,price), org (name,url,logo,same_as) or breadcrumbs (trail [[name,url]...]). Returns the object, its own audit, and a ready
`aeo_visibility_record`	Record an AI share-of-answer panel. YOU (the calling agent) ask each engine the prompts and pass the answers here [{engine,prompt,text,citations}]; AdRails scores mentions, rank vs competitors, citations and sentiment, stores the run, and returns the score plus the change since the last run.
`aeo_indexnow`	Tell Bing, Yandex, Seznam and Naver (IndexNow) that URLs changed. Checks the key file at https://host/.txt first. submit=false (default) only validates. Optionally pass sitemap_since (ISO date) to send every sitemap URL modified since then.
`aeo_ai_traffic`	Read-only. Measure AI-driven traffic from a local web-server log (nginx combined or JSON lines): referrals from ChatGPT, Perplexity, Claude, Gemini and Copilot with landing pages, and AI crawler fetches split into verified / unverified / spoofed (by the providers' published IP ranges and scanner paths).
`aeo_content_gaps`	Read-only. The ads-to-answers bridge: turn the campaign's real paid search terms into the questions people ask, check which of your pages already answer them, and return content briefs for the biggest gaps (facts only from site_facts, never invented).

Hardening applied to every tool (adsops/enterprise_tools.py:harden): role checks (rbac, off until enabled, then deny-by-default), an approval gate on commits (approvals.mode: off | exposure | all; HMAC-signed, expiring tokens; secret from env ADRAILS_APPROVAL_SECRET, 32+ chars), and a SHA-256 hash-chained audit log of every mutating call, refused ones included (audit_chain, default ~/.adsops/audit-chain.jsonl). Monitors deliver to the webhook URL in the env var named by alerts.webhook_env. Nothing here adds spending authority: account changes still go committed state file → adsops_plan → adsops_apply.

New CLI: adsops-mcp init --dir DIR --customer-id ID --campaign-id N --sa KEY.json scaffolds a campaign with a safe policy (caps = today's budget, commits off, enabling forbidden) and makes the first git commit; adsops-mcp --config DIR/config.json doctor reports what's missing, with a fix for each failed check.

Optional per-campaign config for the new tools: negatives (protect, out_of_area, competitor_brands, home_state, llm_command) and landing (towns); top-level intel_config points at your AdRails Intel tenant file (see adsops/intel/README.md).

The policy file

One policy file per campaign, referenced by policy_file in the config. AdRails only ever reads it: no tool writes caps, and adsops_set refuses guards and allow_enable outright. If the state file also carries caps they are ignored — the policy file always wins, and adsops_status says so.

{
  "customer_id": "000-000-0000",
  "campaign_id": 1000000000,
  "guards": {
    "max_daily_budget_usd": 40,
    "min_daily_budget_usd": 5,
    "max_end_date": "2026-12-31",
    "lifetime_spend_pause_usd": 900,
    "daily_spend_pause_usd": 60,
    "max_ops_per_run": 12
  },
  "allow_enable": false,
  "max_guard_age_min": 30,
  "require_plan_id": true,
  "plan_ttl_s": 900
}

Field	Meaning
`customer_id`	Account the campaign lives in; compared with the state file after stripping dashes. A mismatch is refused.
`campaign_id`	The one campaign this policy governs; must match the state file exactly.
`guards.max_daily_budget_usd`	Required. Upper bound of the daily budget, and the budget ceiling checked at plan time.
`guards.min_daily_budget_usd`	Optional. Lower bound of the daily budget; must not exceed the maximum.
`guards.max_end_date`	Required, `YYYY-MM-DD`. Latest end date any plan may set.
`guards.lifetime_spend_pause_usd`	Required. Lifetime spend at which the guard issues a pause.
`guards.daily_spend_pause_usd`	Required. Today's spend at which the guard issues a pause.
`guards.max_ops_per_run`	Required integer. Most operations one run may emit; more is refused.
`allow_enable`	Default `false`. Whether a plan may ever set the campaign to `ENABLED`.
`max_guard_age_min`	Default `null` (check off). When set, any change that adds spend exposure is refused unless the guard heartbeat is younger than this.
`require_plan_id`	Default `false`. When `true`, apply/commit and `adsops_set` with `apply=true` must quote a `plan_id`.
`plan_ttl_s`	Default `900`, range 60–86400. How long an issued `plan_id` stays usable; each one is consumed once.

A missing or malformed field is an error that names the field, not a silent default.

Desired-state file

One JSON file per campaign, referenced by state_file. It says what the campaign should look like; the server computes the diff.

{
  "customer_id": "000-000-0000",
  "campaign_id": 1000000000,
  "campaign_name": "Search - Core",
  "status": "PAUSED",
  "end_date": "2026-11-30",
  "daily_budget_usd": 20,
  "negatives_add": [
    {"text": "free", "match_type": "BROAD"},
    {"text": "cheap", "match_type": "PHRASE"}
  ],
  "guards": {
    "max_end_date": "2026-12-31",
    "min_daily_budget_usd": 5,
    "max_daily_budget_usd": 40,
    "lifetime_spend_pause_usd": 900,
    "daily_spend_pause_usd": 60,
    "max_ops_per_run": 12
  }
}

Field	Meaning
`customer_id`	Account the campaign lives in; dashes are stripped before use.
`campaign_id` / `campaign_name`	The one campaign this file owns. Both must match live state or the run aborts.
`status`	Desired status, `ENABLED` or `PAUSED`. `ENABLED` additionally requires `allow_enable` in the policy.
`end_date`	Desired end date, `YYYY-MM-DD`, bounded by `guards.max_end_date` and not in the past.
`daily_budget_usd`	Desired daily budget, bounded by the min/max guard band.
`negatives_add`	Append-only list of `{text, match_type}`; match type is `BROAD`, `PHRASE` or `EXACT`. Nothing here removes a keyword.
`guards`	Read only when the campaign has no `policy_file` (legacy mode). With a policy file configured, these are ignored.

Keep these files in git. That is what makes "reviewed desired state" real: git diff is the change review, and require_clean_git makes the server refuse to act on uncommitted edits.

ChatGPT Ads

AdRails also runs OpenAI (ChatGPT) Ads accounts through the same plan, validate and commit path. The tools are named adsops_openai_*. Full guide: docs/OPENAI-ADS.md.

One API key per ad account. It comes from 1Password, a file or an env var. Any number of openai_ads accounts can sit in one config.
New campaigns, ad groups and ads are always created paused. Each create carries an Idempotency-Key, so a retry cannot duplicate it.
Caps live in an immutable policy file:
- max daily and lifetime budget
- max cumulative spend per campaign key, and optionally for the whole account
- max bid
- allowed destination domains and required UTM parameters
- no archiving unless allowed
Activating a campaign needs a human approval block in the committed desired-state file. It is single-use and bound to the campaign name. Activation also needs a fresh guard heartbeat and room under the cap, assuming 2x daily overdelivery.
adsops_openai_guard only pauses. It uses live budgets and bids, counts every campaign AdRails created for the key, and pauses an orphaned one. If spend can't be read, it fails closed instead of reading $0.
Helpers: image upload, the brand icon (warns that a brand change can pause delivery until review), pixel plus a lead_created conversion event, and a validate_only test event.
Growth suite: adsops_openai_ad_check (chat_card limits, allowed domains, required UTM, duplicates), adsops_openai_draft_campaign (a business brief becomes a paused, capped desired state the engine accepts), adsops_openai_ab_report (winner with intervals and Bonferroni-corrected significance), adsops_openai_send_conversions (CRM leads to the Conversions API, hashed locally, validate-only unless commit=true) and adsops_openai_audience (hashed customer lists to custom audiences, plan-only unless commit=true).

Live status (2026-11-06): the read paths, live spend reads, the guard and validation ran against a real OpenAI Ads account in read-only mode (live proof); that run found and fixed a full-hour insights rule the mock had missed. Creates, image upload, activation and conversion/audience commits are tested offline (tests/qa_openai_ads.py, tests/qa_openai_suite.py) but have not yet run live. Section 9 of the guide lists what is still unverified.

Local business and Google LSA

For a small service business (plumber, HVAC, roofer, cleaner, lawyer...) that wants leads from Google and AI assistants and runs everything from its own Claude. Setup is one prompt in Claude Desktop's Code tab:

Set me up with AdRails for my business. Read https://adrails.dev/start/ and follow the steps for Claude.

Claude runs adsops-mcp setup --business "Name" --install-desktop (workspace in ~/AdRails, registered in Claude Desktop with a backup of its config), interviews the owner, builds the plan, and later adsops-mcp connect --config ~/AdRails/config.json --client-file <Desktop-app OAuth client JSON> (one Google sign-in: loopback on 127.0.0.1 + PKCE, refresh token saved chmod 600 beside the config, the reachable Google Ads accounts added). No ad account is needed to start: the lead book, plan, AEO and reviews work without one.

Tool	What it does
`adsops_local_profile`	questions to ask the owner; saved profile; max cost per lead the business can afford
`adsops_local_plan`	generic 90-day growth plan customised from the profile (4 stages, every task names its tool); progress
`adsops_leads`, `adsops_leads_due`	one lead book for every channel (dedupe by source id/phone/email), stages, follow-ups, pipeline by source, CSV, won jobs -> offline conversions; speed-to-lead list
`adsops_lsa_leads`	Local Services Ads leads + calls/messages, answered/missed, first-response time, junk flags; syncs into the lead book
`adsops_lsa_report`	spend, cost per charged lead vs max, credits, booking/answer rate, missed calls, leads by hour/weekday, advice
`adsops_lsa_dispute`	suggested lead ratings (junk -> dissatisfied + Google's reason, booked -> satisfied); `commit=true` + explicit decisions submits ProvideLeadFeedback
`adsops_lsa_readiness`	license/insurance/background/registration verification, expiry warnings, ranking signals
`adsops_local_schema`, `adsops_local_nap`, `adsops_local_area_page`	LocalBusiness JSON-LD, NAP consistency + citation checklist, service x area page briefs for AI answers
`adsops_reviews`	review link, request/reply DRAFTS (never sent), review velocity vs competitors

MCP prompts start, morning and weekly chain these into the daily routine. Status, exactly: the LSA tools are built against the Google Ads API v24 definitions and tested offline (tests/qa_local_suite.py, 77 checks through the real server); they have not yet run against a live LSA account. LSA budgets, bids, hours and pausing are not in the API (dashboard only). Lead-book, profile and plan files live beside the config (chmod 600); the audit chain records only structural arguments for these tools, never customer names or notes.

Works with Google's official `google-ads-mcp`

Run both servers side by side in the same MCP client. Google's server answers ad-hoc GAQL exploration and never writes; AdRails is the only write path. The agent reads with the official server, proposes a change to a desired-state or policy file, and applies it through adsops_plan / adsops_apply — with caps, a validate pass, an account lock, and an audit line in between. Both speak JSON-RPC over stdio, so one .mcp.json entry each is the whole setup.

License

Apache-2.0.