Analytics & Reporting Automation
Analytics teams drown in dashboards, manual data exports, and repetitive report generation. MCP Bundles turns your AI into a data analyst that can query multiple systems simultaneously, identify insights you might miss, and deliver actionable reports that drive decisions.
What this enables for data and analytics teams
Instead of: Manually exporting data from multiple platforms, creating charts in spreadsheets, writing the same SQL queries repeatedly, and spending hours correlating insights across different tools.
You get: AI that understands your business metrics, automatically identifies trends and anomalies, generates narrative reports, and suggests follow-up analyses—all while maintaining data security and governance.
Real outcomes analytics teams see
- 3-5 hours saved per week per analyst through automated reporting
- 40% faster insight discovery through cross-platform data correlation
- 75% reduction in time spent on routine dashboard creation
- Proactive anomaly detection that catches issues before they become problems
Recommended integrations
PostHog - Product analytics and experimentation
43 tools for event tracking, funnel analysis, cohort studies, user segmentation, and A/B testing insights.
Why it matters: PostHog provides the behavioral data that explains why users do what they do. AI can analyze user journeys, identify conversion bottlenecks, measure experiment impact, and suggest product improvements based on real user behavior.
Plausible - Privacy-first web analytics
21 tools for traffic analysis, goal tracking, and conversion insights without compromising user privacy.
Why it matters: In a privacy-focused world, Plausible provides reliable web analytics. AI can track marketing campaign performance, identify high-value traffic sources, monitor conversion funnels, and correlate web behavior with business outcomes.
Google Analytics - Comprehensive web and marketing analytics
7 tools covering acquisition channels, user behavior, conversion tracking, and attribution modeling.
Why it matters: Google Analytics remains the standard for understanding how users find and interact with your product. AI can analyze complex attribution models, identify marketing channel effectiveness, and correlate web analytics with revenue and product metrics.
Google Search Console - Search engine visibility and performance
20 tools for monitoring search rankings, indexing status, click-through rates, and search query analysis.
Why it matters: SEO drives organic growth, but monitoring requires constant attention. AI can track ranking changes, identify indexing issues, analyze search query performance, and suggest content optimizations based on real search data.
PostgreSQL - Business intelligence and internal metrics
38 tools for complex queries, data exploration, and reporting across your internal data warehouse.
Why it matters: Your business logic lives in your database. AI can safely explore complex schemas, join tables across your data model, generate custom reports, and identify data quality issues—all while respecting your security policies.
Example workflows you can implement
Executive KPI reporting and insights
The problem: Leadership needs weekly updates but analysts spend hours manually compiling the same metrics and narratives.
AI solution:
"Generate this week's executive summary report:
- Key metrics: revenue, user acquisition, retention, churn for the last 30 days
- Compare to previous period and year-over-year
- Identify the top 3 positive trends and top 3 concerning trends
- Correlate changes with recent product launches or marketing campaigns
- Suggest 2-3 specific analyses to investigate further
- Format as a concise executive memo"
What happens: AI pulls data from all your systems, identifies meaningful patterns, and delivers a narrative report that tells the story behind the numbers, freeing analysts for deeper strategic work.
SEO performance monitoring and optimization
The problem: SEO requires constant monitoring of rankings, indexing, and content performance across hundreds of keywords and pages.
AI solution:
"Analyze our SEO performance this month:
- Top 10 ranking improvements and what likely caused them
- Top 10 ranking declines and potential reasons
- Pages with significant traffic changes
- New content opportunities based on search query analysis
- Technical SEO issues that need immediate attention
- Prioritized action items for next month"
What happens: AI continuously monitors your search presence, identifies optimization opportunities, and provides actionable recommendations that actually improve your organic visibility.
Product usage and feature adoption analysis
The problem: Product teams need to understand how users actually use their features, but this requires complex event analysis and cohort studies.
AI solution:
"Analyze feature adoption for our new collaboration tools:
- What percentage of users have tried each feature?
- Which user segments are most/least engaged?
- What behavior patterns predict high adoption?
- Are there usage drop-offs at specific points in the user journey?
- Compare adoption rates across different onboarding flows
- Recommend specific improvements based on the data"
What happens: AI becomes your product analyst, automatically segmenting users, analyzing behavior patterns, and providing insights that drive product development decisions.
Data quality monitoring and anomaly detection
The problem: Data pipelines break silently, and teams don't notice until reports are wrong or decisions are made on bad data.
AI solution:
"Perform data quality health check:
- Check for missing data in critical tables over the last 7 days
- Identify unusual patterns or outliers in key metrics
- Validate data consistency across related tables
- Monitor for schema changes that might break reports
- Alert on any data freshness issues
- Generate a data quality scorecard for the team"
What happens: AI proactively monitors your data ecosystem, catching issues before they impact business decisions and maintaining trust in your analytics infrastructure.
Getting started
- Start with your primary analytics platform (PostHog for product analytics, Google Analytics for web/marketing)
- Add search visibility tracking (Google Search Console for SEO monitoring)
- Connect your data warehouse (PostgreSQL for internal business metrics)
- Enable privacy-focused web analytics (Plausible for GDPR-compliant insights)
Quick setup checklist
- Set up MCP Bundles account and API credentials
- Connect primary analytics platform (PostHog or Google Analytics)
- Enable Google Search Console for SEO tracking
- Configure PostgreSQL database access for internal metrics
- Add Plausible for privacy-compliant web analytics
- Set up AI client (Cursor or Claude Desktop)
- Test with sample reporting workflows above