This guide brings together practical example questions you can ask the MCP along with helpful tips and tricks, and frequently asked questions, for getting the most reliable and efficient results from Polar. It’s designed to show not only what you can ask Polar to retrieve or analyze, but also how to structure your workflow, maintain context, and verify outputs so your insights are accurate and easy to use.
Always prioritize projects over direct chats
Always prioritize projects over direct chats
The Problem: Running analyses in standalone chats leads to:
Chats maxing out faster than expected
Loss of context when starting new conversations
Uncertainty about whether previous work is being considered
Frequent errors and hallucinations
Time wasted double-checking numbers
The Solution: Create a Project from day one for any multi-step analysis or ongoing work. It will give you:
Persistent Context: Your rules, instructions, and context carry through all chats within the project
Consistency: Reduces errors and hallucinations by maintaining clear parameters
Efficiency: No need to re-explain your data structure or requirements in each new chat
Confidence: Numbers and analyses remain consistent across conversations
Organization: All related work stays in one place
Here’s an example of how to create a project: Executive Summary Prompt
Always review check the tool calls that the LLM is doing, to ensure accuracy
Always review check the tool calls that the LLM is doing, to ensure accuracy
For example in the conversation below, Claude assumed that the date range should be January 1 to January 14th 2025.
By checking the generate_report tool, we could tell that the date range used shouldn’t be this one. Same goes for the metrics and dimensions it’s using to generate your answers.
Tool Descriptions can be found under Settings > Connectors > Polar > Configure . Tool descriptions help the AI model (and hopefully you) understand what each tool does, what it's capable of, and when it should be used.
Tool Descriptions can be found under Settings > Connectors > Polar > Configure . Tool descriptions help the AI model (and hopefully you) understand what each tool does, what it's capable of, and when it should be used.
Looking at the tools available in this conversation, for example, the Polar:generate_report tool has a description explaining it generates comprehensive analytics reports and specifies exactly what inputs are required (metrics, dimensions, date ranges, etc.) and what output format to expect (JSON report data).
Style your outputs by telling Claude your brand guidelines and attaching a logo
Style your outputs by telling Claude your brand guidelines and attaching a logo
Create a project with instructions, and attach a Brand Guidelines text file.
# Brand guidelines
Logo: bear-logo-svg.txt (SVG inside a text file)
- Display at minimum 60px height for optimal readability
- Maintain aspect ratio when scaling
Primary text: #0F3328
Secondary text: #446058
Accent color: #00A574
Background color: #FFFFFF
Use a light background, very few boxes or borders, just dividers or shaded background where absolutely necessary.
Body font: Source Sans 3 Light 300
Heading font: Source Serif 4 SemiBold 600
Both fonts available from Google Fonts
For your logo, attach an SVG with a .txt extension. If you upload a PNG or JPG it will “see” the image and try to recreate it instead.
Result:
Example questions to ask the MCP
Example questions to ask the MCP
Run an analysis of net sales by Top 5 Cities (using Billing Address), ranking by year from 2020 to 2025 full year
What is the ad spend for the UK region in August 2025?
If you’ve created a “UK” view in Polar, it will find this and use it as a filter.
Can you give the EU Total Sales for last month?
Can you look at the [dashboard name > report name] report and give me a summary?
Replace the dashboard and report name with something you’ve built in Polar.
FAQs
FAQs
Is this just ChatGPT on top of APIs?
No. Claude & ChatGPT connect to Polar’s semantic layer, not raw endpoints. That’s why answers are consistent, accurate, and repeatable.
For users that have access to multiple tenants via Workspaces, what are the implications for their MCP usage? Will all users on both accounts be able to see all of his recent chats?
MCP (e.g. with Claude) - Claude queries Polar in a read-only manner. No trace of your chats with Claude are left inside Polar. Claude chats are only visible to others if you share them manually via Claude's sharing features.How do you make sure the AI uses the right metrics or date ranges?
The MCP guides Claude to confirm metrics, filters, and date ranges before analysis. You’ll see it validate context live in your chat.
Always double check the tool calls that the LLM is making. For example in the conversation below, Claude assumed that the date range should be January 1 to January 14th 2025.
Will it work with my custom definitions?
Yes. Your custom metrics, dimensions and views are being fed into Claude via the tools
Is my data secure?
The security of your data depends on your Claude subscription type:
Commercial Products (Claude for Work - Team/Enterprise, API)
✅ No training on your data by default - Anthropic does not train models on data from commercial products
✅ Your inputs and outputs remain private unless you explicitly opt-in to development programs
⚠️ Exceptions: Data may be reviewed if you provide feedback or if conversations are flagged for policy violations
Consumer Products (Claude Free, Claude Pro)
⚠️ Different privacy policies apply - Claude Pro is classified as a consumer product, not commercial
⚠️ Consumer products follow different data usage practices than commercial offerings
📝 Check your Privacy Settings to confirm your data usage preferences
Recommendations
For sensitive customer data: Consider using Claude for Work (Team/Enterprise) plans for commercial-grade privacy protections
Review privacy settings: Check your current settings at Anthropic's Privacy Policy
Isolate sensitive data: Take precautions when connecting Claude to services with highly sensitive information
Stay informed: Privacy policies and data handling practices may evolve, so review them periodically
TLDR: If you're handling sensitive customer data through MCP tools, a commercial Claude plan provides the strongest privacy guarantees with no model training on your data by default.
This resource helps you approach Polar with clarity and purpose — giving you both the “what to ask” and the “how to ask it well.” By following the example questions and adopting the Tips & Tricks guidance, you’re more likely to get accurate, consistent, and polished results from Polar’s AI-driven analytics tools.