Once you've set up the Optmyzr MCP integration with Claude, you're ready to start analyzing your ad accounts using natural language.

Depending on how specific you want the output to be, you can use these prompts.

Detailed prompt:

Summarize overall account performance for the last 30 days by identifying the top positive movers and the biggest declines across campaigns, ad groups, or keywords. Highlight the three strongest improvements based on metrics such as conversions, CPA, or ROAS, and the three biggest drops, such as conversion declines, CPA spikes, or CTR drops. For each insight, show the current value versus the prior 30-day period, the percentage change, and a short explanation of why this movement matters. Based on these changes, recommend three priority checks the user should review first, such as budget constraints, disapproved ads, landing page issues, or impression share losses, and provide one clear next action for each.

Detailed prompt:

Display impressions, clicks, cost, conversions, and CPA on a week-over-week basis for the last four weeks. Automatically flag any metric that moved more than ±15% from the previous week. For each flagged change, show the previous value, the current value, and the percentage difference, and identify which campaigns contributed most to the shift. Add a short explanation of potential causes, such as budget changes, bid adjustments, seasonality, or impression share fluctuations, and include one or two suggested actions for each anomaly.

Detailed prompt:

Analyze campaign-level performance for the last 30 days and identify which campaigns drove the most conversions and which lost the most momentum compared to the prior 30-day period. For each campaign, show current versus previous conversions, percentage change, CPA trend, and CTR or impression trend. Classify campaigns as scaling, declining, or stable. For campaigns that are declining, provide two likely diagnostic explanations such as creative fatigue, rising competition, targeting inefficiencies, or landing page issues, and suggest one next step to investigate.

Detailed prompt:

Tell me how the budget is driving results this month — campaign-level performance & spend distribution for the last 30 days. Generate a Performance Overview report that highlights overall account health and campaign-level performance. Include the following outputs:

Detailed prompt:

Summarize account health and campaign-level performance. Include:

Detailed prompt:

Scan all active ads for the selected account over the last 30 days and list ads with the lowest CTR, lowest impressions, and highest relative drop in CTR versus the prior 30 days. For each ad show: ad title, campaign, ad group, CTR, impressions, clicks, and change vs prior period. Provide 1–2 quick tests for each (headline tweak, CTA change, landing check) and a short A/B test suggestion (control vs test headline or description).

Detailed prompt:

Identify keywords and ads with Quality Score ≤ 5 (or, if history is missing, with the largest negative delta) for the last 30 days. For the top 10 offenders show the QS, CTR, expected landing experience signals (if available), and suggestions: rewrite ad copy, improve landing relevance, or change match type. For each suggested fix, include the rationale and one short metric to observe (e.g., CTR change after 7 days). Provide the link to the Quality Score tracker tool for an in-depth analysis.

Detailed prompt:

Find ad groups whose average Quality Score is below 5 or which dropped more than 1 point in the last 30 days. For each ad group, list the lowest-performing keywords and ads, the average CTR and conversion rate, and two prioritized actions (copy refresh, keyword pruning) with estimated expected effects. Provide the link to the Quality Score tracker tool for an in-depth analysis.

Detailed prompt:

Analyze the highest-engagement ads in the account based on CTR, conversion rate, and impression-weighted performance over the last 30 days. Identify common patterns across top-performing creatives, such as phrasing style, emotional triggers, benefit framing, urgency cues, or keyword usage. Using these patterns, generate three stronger headline suggestions for the user's top ads. For each suggested headline, include a short explanation of why it should perform better and propose a simple A/B test structure, specifying the control (current headline) and the test (new headline), along with the primary metric to monitor.

Detailed prompt:

Tell me where ad spend is (or isn't) converting — Ad Performance Insights report for the last 30 days. The report should contain:

Detailed prompt:

For the last 30 days, list keywords with high spend (top X% by spend or above a set threshold) but zero or near-zero conversions and a low conversion rate versus the account average. For each, show spend, clicks, impressions, CPC, and a suggested action (pause, lower bid by X% if manual bidding is enabled, or add as negative). Do this by creating a Rule Engine strategy.

Detailed prompt:

Extract search queries from the last 30 days and flag queries with high impressions but CTR below threshold or zero conversions. Provide a ranked list of candidate negatives.

Detailed prompt:

Analyze all search queries from the last 30 days and identify those that received impressions but resulted in zero clicks or zero conversions. Prioritize queries with the highest impression volume and spend, as these represent wasted visibility or budget. For each query, show impressions, clicks, conversions, cost, and the campaign and ad group it belongs to. Based on relevance patterns, intent mismatches, or semantic drift, recommend whether each term should be added as a negative keyword and at what match type. Include a short rationale for each recommendation.

Detailed prompt:

Identify keywords with low impression or click volume that have converted once or twice in the last 30 days. These should be treated as early indicators of high intent. For each keyword, show impressions, clicks, conversions, CPA, match type, and current bid. Analyze whether performance is limited by low bids, restrictive match types, poor ad relevance, or limited reach. Suggest 1–2 optimization actions per keyword, such as loosening match types, increasing bids, isolating into a SKAG-style group, or writing more targeted ad copy.

Detailed prompt:

Scan all active keywords from the last 30 days and identify those with disproportionately high spend but very low click volume. For each keyword, show impressions, clicks, CTR, cost, CPC, and match type. Highlight keywords where the CPC is high but engagement is weak, indicating possible relevance or targeting issues. For each keyword, suggest one corrective action — such as tightening match types, improving ad relevance, lowering bids, or pausing entirely — and explain why that action is appropriate.

Detailed prompt:

Identify keywords with high impression volume but low CTR over the last 30 days, indicating potential relevance mismatches between user intent and ad messaging. For each keyword, show impressions, CTR, clicks, conversions, and the associated ad copy. Analyze whether the issue is likely due to vague intent, overly broad match types, misaligned messaging, or weak value propositions. For each case, suggest 1–2 specific improvements, such as refining match types, rewriting ads to better mirror user intent, or splitting keywords into more granular themes.

Detailed prompt:

For the campaign with the highest spend in the last 30 days, deliver: (a) a KPI card set (spend, conversions, CPA, CTR), (b) three specific tactical tests curated for the campaign, (c) expected metric impact and monitoring period, and (d) a suggested safe rollout plan.

Detailed prompt:

Flag campaigns where daily spend is in the top 90% while conversions are in the bottom 15%. For each, show spend/conversion ratio and CPC trend. Recommend general triage steps along with a "hold-or-continue" action (Immediate hold / Reduce by X% / Monitor) and justification.

Detailed prompt:

Identify the campaigns with the highest increase and the highest decrease in conversions compared to the previous week. For each campaign, show this week's versus last week's conversions, the percentage change, and supporting shifts in CPA and CTR. Classify each campaign as gaining momentum or losing momentum. For gainers, provide one scaling or expansion suggestion. For decliners, provide one diagnostic action to identify the root cause.

Detailed prompt:

How much budget can I safely shift to top performers in the last 30 days? Identify high-performing campaigns with strong ROAS or conversion rates and unused budget capacity. Focus on which conditions qualify a campaign as a good candidate to receive more budget — such as high efficiency, low current budget utilization, impression share below 85%, and stable performance trends. Also identify potential source campaigns that are overspending or showing poor returns (e.g., CPA significantly above average, pacing too aggressively). Emphasize the signals and thresholds that define safe reallocation in a summary so I can review which campaigns meet the criteria and why.

Detailed prompt:

Which campaigns are underspending but converting well — where could I safely scale the budget? For the last 30 days, identify campaigns with a conversion rate above the account average and budget utilization under 60%. For each candidate, show conversions, CPA, spend, % budget used, remaining budget, recent trend (conversion and CPA trajectory), and an estimated incremental conversion range if budget were increased (conservative / likely / optimistic). Also flag confidence and any risk indicators (low volume, high CPA volatility, recent tracking or creative changes) so you can quickly review which campaigns truly have safe scaling capacity.