Remember, AdLabs is already a powerful tool and you might not need to go through the MCP for everything.
Questions like these below are much more easily done from AdLabs and would not be great use cases for the MCP.
"Show me my top 10 keywords by spend in the last 30 days"
"Which campaigns have ACOS above 40% this month?"
"Tag all campaigns containing 'Brand' in the name as 'Branded'"
Where the MCP becomes valuable is when the task requires combining multiple steps, cross-referencing different data sets, applying judgment, or doing something repetitive across a large number of entities.
Think of it as an advanced tool for the work that's tedious, multi-layered, or hard to template inside a dashboard.
Some examples of where the MCP shines:
"Run an n-gram analysis on my search terms from the last 30 days, group by ad type, and flag any patterns that suggest wasted spend"
"Look at all my campaigns with ACOS above target, check if the issue is at the keyword level or placement level, and give me a prioritized action plan"
"Compare my Sponsored Products performance week-over-week, flag anything that moved more than 20%, and draft a summary I can send to my client"
"Audit this profile and tell me where the biggest opportunities are"
Claude supports "skills". These are reusable instructions saved as markdown (.md) files that teach Claude how to do specific tasks. Think of them as SOPs for your AI.
For example, you could create a skill that tells Claude exactly how to run your Monday morning performance review: which metrics to pull, what thresholds to flag, how to format the output, and what to compare against. Instead of typing all of that every time, you just invoke the skill.
Skills can reference AdLabs data, define step-by-step workflows, and include your specific business logic.
Claude also supports scheduled tasks that run automatically on a set interval. Combined with the AdLabs MCP, you can set up recurring workflows like:
Every Monday morning, pull a weekly performance summary across your top profiles and flag anything outside your target ACOS range
Every week, check for keywords with high spend and zero conversions in the last 7 days and send me a detailed report
Once a month, run an n-gram analysis on search terms to surface new negative keyword candidates
Scheduled tasks let you automate the repetitive analysis work so you can focus on the decisions.
The best way to learn what's possible is to experiment. Here are some directions to explore:
Multi-step analysis: Ask the AI to diagnose a problem rather than just show you data. "Why did ACOS spike last week?" is more powerful than "Show me ACOS by campaign."
Auditing: The MCP has a built-in audit tool that generates a health scorecard with period-over-period comparison. Great for new account onboarding or quarterly reviews.
Search term mining: N-gram analysis surfaces patterns in your search terms that you'd miss scrolling through raw data. Useful for finding negative keyword candidates or identifying new opportunities.
Bulk operations with logic: "Pause all targets with zero sales and more than $50 spend in the last 60 days" -- describe the criteria in plain English and let the AI handle the filtering and execution.
Client prep: Pull key metrics, flag anomalies, and draft talking points before a client call without building a manual report.
Cross-referencing: Combine data from different views (e.g., search terms + placements + change history) to answer questions that no single dashboard view can answer on its own.
