To manage google and meta ads with claude, connect Claude to an MCP server that can access each ad platform, then use it to review performance and prepare changes for your approval. A reliable workflow keeps Google and Meta data distinct, checks recommendations against shared business goals, and applies changes only after you confirm the exact account, campaign, and action.
Define what Claude is allowed to do
Start with a job, not an open-ended instruction to “optimize my ads.” Choose a bounded task such as finding campaigns with rising spend and falling qualified-lead volume. Decide whether Claude may only read data, draft proposed edits, or execute approved changes. Separate analysis from action so a useful diagnosis does not automatically become a live account change.
Write down the operating boundaries
Before connecting an account, record which business, ad account, and campaign set are in scope; which metrics count as success; and who approves edits. For an agency, set these rules separately for each client. If you use revenue or lead quality as the goal, specify where that information comes from rather than treating every platform-reported conversion as equivalent.
- Read scope: identify the accounts and reporting period Claude may inspect.
- Change scope: name allowed edit types, such as pausing a campaign or proposing a budget adjustment.
- Approval scope: require a human to review the proposed value and target before execution.
- Stop conditions: specify when Claude must ask instead—for example, missing conversion data or a campaign outside the agreed scope.
This is also where you decide whether to build an MCP connection or use a hosted one. MCP defines a way for AI applications to connect with tools and data sources, but the tools and permissions exposed depend on the server and client configuration; check the MCP introduction before assuming a connection can perform a particular task.
Connect Claude to both advertising platforms
Claude needs an MCP server that exposes the Google Ads and Meta Ads operations you intend to use. A connection is not the same as unrestricted account access: confirm which accounts are authorized and which read or write tools the server provides. In Claude, setup options depend on the client and the server’s connection method; consult Anthropic’s MCP connector documentation for its documented connector workflow.
Check the tools before trusting the connection
For a self-managed integration, compare the server’s available operations with the APIs you need. Google’s API documentation distinguishes reporting from mutation workflows, and the Google Ads API getting-started guide describes the prerequisites for working with that API. Meta’s Marketing API documentation is the reference for its platform’s available marketing operations. Neither platform’s API documentation guarantees that a particular MCP server exposes every operation.
If you want a hosted option rather than maintaining the connection yourself, NotFair offers a Google Ads MCP and a Meta Ads MCP for AI clients including Claude. Verify each server’s current tool list and authorization flow before connecting production accounts; availability of a platform API feature does not, by itself, establish that a server supports it.
- Confirm the authorized business and ad-account IDs.
- Check whether each exposed tool is read-only or can change account state.
- Test a reporting request before asking for any write operation.
- Keep credentials and approval controls within the account owner’s process.
Create a comparable reporting brief
Ask Claude for a defined report rather than a broad performance summary. Include the date range, comparison period, campaign scope, account currency, and the business outcome you care about. For platform reporting details, Google documents its reporting query model in the Google Ads API reporting overview; use the relevant Meta reporting fields exposed by your connected server for the other account.
Do not add unlike metrics together. A Google Ads conversion and a Meta-reported conversion may use different attribution settings, event definitions, or reporting delays. Present platform totals side by side first. If you need a combined business view, reconcile them against a declared source of truth, such as CRM-qualified leads or recorded sales, and state the limits of that reconciliation.
A useful report request might say: “For the last 14 complete days, compare each platform with the prior 14 complete days. Show spend, impressions, clicks, reported conversions, cost per conversion, and the available conversion action or event. Separate Google from Meta, flag missing data, and do not recommend edits yet.” The time span here is an illustrative starting policy, not a universal reporting window; lengthen it when conversion volume is sparse or results are delayed, and shorten it only when the business has enough timely data to make the comparison meaningful.
Ask for a diagnosis with evidence
Have Claude identify a small number of material changes in the report, then require it to show the evidence behind each one. A diagnosis should name the account and campaign, compare the same metric across the chosen periods, and distinguish an observation from a likely explanation. “Spend rose while recorded leads fell” is an observation; “the landing page caused the decline” is a hypothesis that needs further evidence.
Use a worked example
Illustrative example: A lead-generation team sees Google campaign spend rise from $1,000 to $1,200 between two equal reporting periods, while its recorded conversions fall from 40 to 30. Meta spend and conversions are reported separately, and the team’s CRM shows fewer qualified leads overall. Claude can calculate that Google’s platform-reported cost per conversion increased from $25 to $40, then flag the change for investigation. That does not prove the campaign caused the CRM decline: the team should check tracking, lead qualification, landing-page changes, and the comparable Meta trend before deciding what to change.
- Ask Claude to list the metric, values, periods, and source for every flagged issue.
- Ask it to identify missing or inconsistent tracking before attributing cause.
- Request one or more testable explanations, not a single asserted cause.
- Reject recommendations that do not identify the affected account and campaign.
Keep diagnosis separate from attribution unless the data actually supports an attribution claim. Google’s reporting model and Meta’s reported results are useful for platform-specific decisions, but they are not automatically a deduplicated cross-channel measure of business impact.
Turn a recommendation into an approved change
For each proposed edit, require a change preview with the target, current value, proposed value, reason, expected risk, and a way to verify the result. Google’s mutating overview describes how changes are made through the Google Ads API; the exact write operations available to Claude still depend on the MCP server and its permissions. Treat a Meta change with the same review discipline, and confirm the corresponding operation is actually exposed before requesting it.
| Control | Illustrative starting policy | Adjust when |
|---|---|---|
| Daily budget edit | Require approval for every proposed change; cap a single approved adjustment at 10%. | Change the cap only after reviewing spend volatility, campaign maturity, and the cost of an unintended shift. |
| Pause recommendation | Require two independent signals, such as tracking health plus a sustained business-metric decline. | Use a longer evidence window when conversions are delayed or low-volume; investigate tracking first if signals conflict. |
| Post-change review | Schedule a check after 7 complete days. | Review sooner for overspend or delivery problems; wait longer when the conversion cycle needs more time. |
These numbers are illustrative starting policies, not platform benchmarks or universal safeguards. Adjust them based on account risk, conversion lag, spend variability, and how quickly reliable outcome data arrives. For a change with uncertain impact, use a smaller reversible edit or leave it as a recommendation for a human to implement.
Before execution, read the proposed change back in plain language and obtain explicit approval. Afterward, record the action and verify that the intended value changed in the right account. Keep a rollback plan, but do not assume every platform change can be reversed without consequence—for example, lost delivery history or time-sensitive opportunities may not be recoverable.
First, run a read-only account check
Your first move should be to connect one account from each platform and ask Claude for a read-only inventory: account identity, available reporting tools, date coverage, currency, and the metrics it can retrieve. Do not authorize or request changes until you can verify that inventory against the ad interfaces and your own reporting definitions.
Then run the same bounded comparison for both platforms, review the evidence, and choose one low-risk recommendation to evaluate manually. If you need hosted connections and approval-gated workflows, NotFair; confirm the current tool coverage and safeguards fit your account process before adopting it.
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