The Polar MCP (Model Connection Protocol) lets you connect your Polar Analytics data directly to AI tools like ChatGPT or Claude, as well as automation platforms like Make and n8n. With MCP, you can query your Polar workspace data conversationally, automate reporting, and generate actionable insights in seconds — without needing SQL or dashboards.

To help you make the most of this capability, this article covers key tips, tricks, and best practices for using MCP efficiently and effectively.

You’ll learn how to:

Write precise prompts that return accurate, useful results.
Structure your queries for deeper business insights.
Combine Polar metrics creatively to unlock advanced analyses.

Crafting Effective MCP Prompts

The quality of your results depends on the quality of your prompts. While MCP can interpret natural language, clear, specific instructions yield the most accurate insights.

1. Be Explicit About What You Need

The more context you provide, the better MCP can interpret your request. Include metrics, timeframes, and segments in your prompt.

Example:
Instead of:

“Show me my ad performance.”
Try:
“Show me ad spend, ROAS, and CAC by channel for the last 30 days compared to the previous 30 days.”

2. Reference Time Periods Clearly

Always define your timeframe explicitly. MCP understands relative and fixed timeframes such as:

“Last 7 days”
“This month”
“Q3 2024”
“Between January 1st and February 15th”

Tip: Adding comparison periods (e.g., “vs last month”) helps MCP highlight trends and deltas automatically.

3. Ask for Explanations or Recommendations

Don’t just ask for numbers — ask for interpretation or next steps. MCP can summarize insights in context.

Example:

“Analyze my ad performance by channel for the last 30 days. Summarize the trends and recommend actions to improve ROAS.”

This turns MCP from a reporting tool into a virtual analyst.

4. Combine Multiple Metrics in One Query

You can request more than one KPI in a single prompt to reveal relationships between performance indicators.

Example:

“Show me revenue, profit margin, and email-attributed conversions for my top five products last month.”

Structuring Queries for Deeper Insights

Beyond basic performance summaries, MCP can help you uncover strategic, cross-functional insights when you phrase prompts in a structured way.

1. Use Comparative Analysis

Ask MCP to compare data across time periods, channels, or campaigns to understand what’s driving change.

Examples:

“Compare Meta and Google Ads performance over the last two months.”
“Show me how CAC has changed week-over-week and explain the cause.”
“Which channel has the biggest improvement in ROAS this quarter?”

Comparative prompts encourage MCP to analyze patterns and contextualize changes automatically.

2. Request Segmented Data

Segment your analysis to understand performance at a granular level.
Try asking for breakdowns by:

Channel (Meta, Google, TikTok, Email, etc.)
Device (mobile vs desktop)
Customer type (new vs returning)
Product or SKU

Example:

“Show me revenue and CAC for new vs returning customers in the last 60 days.”

3. Leverage Business Dimensions

Polar’s semantic layer lets you query metrics across dimensions (e.g., date, campaign, product, channel).
This allows for multi-dimensional analysis like:

“Show daily revenue and ad spend trends for my top 3 performing channels.”

You can also ask for aggregation levels such as “weekly,” “monthly,” or “by campaign.”

Pro Tips for Power Users

Once you’re comfortable with basic prompts, these advanced techniques will help you maximize MCP’s value:

1. Chain Queries for Iterative Analysis

You can refine your analysis step by step. After getting initial results, ask follow-up questions:

“Now break down that revenue by channel.”
“Which campaigns had the highest CPC?”
“What’s the ROAS trend for those campaigns?”

MCP retains context within the same session, so iterative queries are a powerful way to drill deeper without rephrasing everything.

2. Use Natural Language — But Stay Focused

MCP understands conversational language, but avoid vague terms like “good,” “bad,” or “interesting.”
Instead, be directive — specify the outcome or metric type you want to understand.

Example:

“Highlight the top three campaigns with the highest CAC increase compared to last month.”

3. Create Saved Prompts or Templates

If you find yourself asking the same question weekly — such as, “What’s my performance by channel this week?” — save it as a template in your preferred AI tool (e.g., ChatGPT).
This helps maintain consistency and saves time for recurring reporting.

4. Pair MCP with Business Workflows

MCP is not just for ad-hoc analysis — it can power your automation stack:

In Make or n8n, set workflows to automatically post daily performance summaries to Slack.
In ChatGPT, use the MCP connection to generate weekly reports or executive summaries.

This lets you use MCP as both an analytics assistant and a process automation engine.

5. Reference Polar’s Metric Directory

If you’re unsure which metrics MCP supports, review the Metric Directory for the MCP.
It provides a full list of queryable KPIs and definitions to improve your prompt accuracy.

The Polar MCP is a powerful bridge between your business data and intelligent analysis — but the value you get depends on how you use it. By mastering structured prompts, comparative questions, and automation workflows, you can turn MCP into your always-available marketing and growth strategist.

Key takeaways: