This page gathers Catchr MCP usage examples in one place.
It includes:
- Common workflows
- Expected outputs
- Copy-ready prompts
- Support boundaries
- Escalation guidance
Before using these prompts, make sure the Catchr MCP app is already installed in your AI assistant.
If the app is not installed yet, follow the installation guide first and then come back to this page.
Also, make sure:
- Your sources are connected in app.catchr.io
- The account you want to analyze is available in Catchr
- You know which date range you want to review
Best fit for:
- Agencies managing multiple clients
- Teams handling several accounts per platform
- Account managers preparing client-facing reports
Expected output:
- Client summary
- Per-channel highlights
- Key wins
- Risks and underperformance
- Next-step recommendations
Prompt:
Using the connected Catchr data for [Client Name], create a weekly performance report for the last 7 days.
Include GA4, Google Ads, Facebook Ads, and any other connected paid social channels.
Compare performance to the previous 7 days.
Structure the answer with:
- executive summary
- channel breakdown
- wins
- issues
- recommendations
Make the report visually clear and easy to share with a client.
Use tables where relevant and suggest the most relevant charts to visualize the results.Expected output:
- List of clients with the strongest and weakest performance
- Accounts with unusual drops or spikes
- Priority actions by the client
Prompt:
Using my connected Catchr data, review all available client accounts for the last 7 days.
Identify:
- the 5 clients needing the most urgent attention
- the 5 strongest performers
- the biggest spend anomalies
- the biggest conversion or ROAS drops
Return the answer as a prioritized list for an agency account manager.
Make the output visually easy to scan, use tables where relevant, and suggest the most useful charts.Expected output:
- Benchmark table
- Outlier accounts
- Common patterns
- Strategic recommendations
Prompt:
Using all connected Google Ads accounts, benchmark performance for the last 30 days.
Compare spend, conversions, CPA, CTR, CPC, and ROAS.
Identify outliers and explain which accounts deserve deeper review.
Return the answer with a benchmark table, clear account groupings, and suggested charts for comparison.Expected output:
- Clear business summary
- Prioritized recommendations
- Expected impact
- Suggested next tests
Prompt:
Using the connected Catchr data for [Client Name], act like a senior paid media strategist.
Review the last 30 days and provide client-ready recommendations.
Prioritize actions by expected impact and keep the tone suitable for a client presentation.
Make the output visually clear, concise, and presentation-ready.
Suggest the best charts to support the recommendations.Best fit for:
- Marketing teams managing their own acquisition channels
- Paid media teams doing weekly reviews and performance follow-up
- Internal reporting and pacing checks
Expected output:
- Top-level performance summary
- Best and worst channels
- Main changes versus the previous period
- Recommended next actions
Prompt:
Using my connected Catchr data, summarize performance for the last 7 days across GA4, Google Ads, and Facebook Ads.
Compare it to the previous 7 days.
Show spend, clicks, sessions, conversions, revenue, CPA, and ROAS when available.
Then give me:
1. the top 3 positive changes
2. the top 3 issues
3. the next 5 actions to take
Make the output visually clear and easy to present.
Use tables where relevant and suggest the most relevant charts to visualize the results.Expected output:
- Possible issue areas
- Platform or campaign breakdown
- Clear anomalies to investigate
- Action checklist
Prompt:
Using my connected Catchr data, identify the most important paid media issues in the last 7 days across Google Ads and Facebook Ads.
Compare performance with the previous equivalent period.
Look for:
- unusual spend increases or drops
- conversion drops
- CPA increases
- ROAS declines
- CTR declines
- CPC increases
Return:
1. the main issues
2. the campaigns affected
3. the likely reasons
4. the urgency level
5. the next actions
Make the output visually easy to scan.
Use a prioritized table if relevant and suggest the most useful validation charts.Expected output:
- Channel comparison table
- Differences between platform data and GA4
- Interpretation notes
- Suggested follow-up checks
Prompt:
Using my connected Catchr data, compare GA4, Google Ads, and Facebook Ads for the last 30 days.
Highlight where platform-reported performance differs from GA4.
Explain the likely reasons in plain English for a marketing manager.
Return the answer with a comparison table and suggest the most relevant charts to visualize the differences.Expected output:
- Executive summary
- KPI highlights
- Risks
- Opportunities
- Recommended decisions
Prompt:
Using my connected Catchr data, create a short executive summary for the last 30 days.
Keep it concise and business-oriented.
Include:
- what improved
- what declined
- the biggest risks
- the biggest opportunities
- what we should do next
Make the output visually clear and easy to paste into a leadership update.
Suggest the most relevant charts to include.Best fit for:
- Teams using Shopify, Magento, WooCommerce, PrestaShop, or CommentSold
- Teams connecting GA4, paid media, and ecommerce outcomes
Expected output:
- Channel-to-revenue summary
- Traffic versus purchase comparison
- Best and worst acquisition sources
- Funnel drop-off observations
- Recommended next actions
Prompt:
Using my connected Catchr data, summarize ecommerce performance for the last 30 days across GA4, Google Ads, Facebook Ads, and my ecommerce platform.
If available, include revenue, purchases, conversion rate, CPA, and ROAS.
Show which channels are driving the most traffic, the most purchases, and the most revenue.
Then explain the key gaps between traffic performance and e-commerce performance.
Make the output visually clear and easy to present.
Use tables where relevant and suggest the most relevant charts for funnel analysis.Expected output:
- Source comparison table
- Correlation findings
- Performance gaps
- Optimization recommendations
Prompt:
Using my connected Catchr data, analyze the correlation between GA4, Google Ads, Facebook Ads, and my ecommerce platform for the last 30 days.
Focus on sessions, add-to-cart activity, purchases, revenue, CPA, and ROAS.
Highlight where paid media looks strong but ecommerce conversion is weak, and where ecommerce demand is strong but media support is low.
Return the answer with:
1. key findings
2. anomalies
3. optimization recommendations
Make the output visually clear and suggest the most relevant comparison charts.Expected output:
- Best-selling product or category summary
- Campaign support analysis
- Revenue gaps
- Optimization suggestions
Prompt:
Using my connected Catchr data, review the last 30 days of ecommerce performance by product or category.
Connect ecommerce results with GA4 and paid media data.
Show which products or categories drive the most revenue and which campaigns appear to support them best.
Then suggest the top actions to improve performance.
Make the output visually clear and suggest the most relevant charts for product and category analysis.Best fit for:
- Lifecycle teams
- CRM managers
- Teams connecting email, GA4, paid media, and ecommerce
Expected output:
- Email performance summary
- Best and worst campaigns
- Impact on sessions, conversions, and revenue
- Recommendations for the next sends
Prompt:
Using my connected Catchr data, summarize email performance for the last 30 days.
If available, connect email results with GA4 and ecommerce outcomes.
Show the impact of email on sessions, conversions, and revenue.
Then identify the best campaigns, the weakest campaigns, and the top optimization ideas.
Make the output visually clear and easy to present.
Use tables where relevant and suggest the most relevant charts.Expected output:
- Channel comparison table
- Relative contribution to traffic and conversions
- Timing or overlap insights
- Suggested budget or sequencing adjustments
Prompt:
Using my connected Catchr data, compare email, Google Ads, and Facebook Ads for the last 30 days.
Use GA4 as the common reference point when possible.
Show the role of each channel in traffic, conversions, and revenue.
Then explain how these channels appear to work together or overlap.
Make the output visually clear and suggest the most relevant comparison chartsExpected output:
- Retention-oriented summary
- New versus returning user insights
- Email contribution compared with paid traffic
- Next-step testing ideas
Prompt:
Using my connected Catchr data, analyze how email contributes to retention and repeat conversions compared with paid channels.
Focus on returning users, repeat purchases, and revenue contribution where available.
Then provide clear recommendations for the CRM or lifecycle team.
Make the output visually clear and suggest the most relevant retention-focused charts.```Use the MCP for:
- Summarizing connected data
- Comparing accounts, channels, and periods
- Turning data into reporting or recommendations
Do not rely on the MCP alone for:
- Final financial validation
- Billing disputes
- Source-side tracking implementation audits
- Platform permission issues
- Data collection issues caused by disconnected or misconfigured sources
Escalate when:
- The MCP cannot access expected accounts or sources
- Results look incomplete or inconsistent with Catchr data
- A source appears disconnected or out of date
- The user reports missing permissions or authentication issues
- The request depends on a product behavior that the MCP should support but does not
If the issue is not resolved through normal usage, ask the user to contact us in the chat.