Knowledge base

Dataslayer MCP

Can I use Dataslayer MCP to generate queries or formulas?

Ask AI to build your Dataslayer queries for you. Get the exact metrics, dimensions, filters, and date ranges you need, then implement them in seconds.

Yes! One of the most powerful features of Dataslayer MCP is its ability to help you build data queries. Think of it as having an expert data analyst on demand who translates your questions into structured query specifications.

Instead of spending time figuring out which metrics, dimensions, and filters to use, you can simply ask your AI assistant, "What query do I need to get X data?" and it will tell you exactly what to pull.

How It Works

When you ask an AI provider connected to Dataslayer MCP to help you build a query, here's what happens:

You describe what you want: Ask your AI assistant about the specific data you need. Be as detailed as possible about your goal.

The AI structures the query: The assistant analyzes your request and returns a complete query specification, including:

Which metrics to select
Which dimensions to group by
What date range to use
Any filters or conditions to apply
Sorting preferences

You implement it in Dataslayer: Take the structured query specification and configure it manually in Dataslayer's interface. Everything is laid out for you you just need to apply the settings.

The AI does the analytical thinking; you do the quick manual setup. This saves significant time compared to figuring out query structures from scratch.

What You Can Generate

Dataslayer MCP can help you build queries for virtually any marketing data question:

Campaign performance queries:

"What query do I need to see my top-performing Facebook campaigns by ROAS for Q4?"
AI returns: Metrics (spend, revenue, ROAS), Dimensions (campaign name), Date range (Oct-Dec 2024), Sort by (ROAS descending)

Cross-platform comparisons:

"How should I structure a query to compare Google Ads vs Meta Ads spend efficiency?"
AI returns: Separate query specifications for each platform with matching metrics and date ranges

Attribution analysis:

"What metrics and dimensions do I need to analyze first-touch vs last-touch attribution?"
AI returns: Complete breakdown of attribution metrics, customer journey dimensions, and recommended filters

Budget tracking queries:

"Build me a query to track monthly spend against budget targets"
AI returns: Metrics (actual spend, budget), Dimensions (month, campaign), Calculated fields needed

Anomaly detection:

"What query should I run to identify campaigns with sudden CTR drops?"
AI returns: Metrics for CTR comparison, time-based dimensions, filters for threshold detection

Best Practices for Query Generation

To get the most accurate and useful query specifications, structure your requests clearly:

Be specific about your goal: Include details about what insight you're trying to gain, not just what data you want to see.
- Example of a well-structured request: "I need to identify which Google Ads campaigns are underperforming this month compared to last month. What query structure should I use in Dataslayer to surface campaigns where CTR dropped more than 20% and spend is above $1000?"
Mention your analysis context: Tell the AI what you'll do with the data. This helps it recommend the right metrics and breakdowns.
- Example: "I'm preparing a presentation for stakeholders about our Q3 social media performance. What metrics, dimensions, and date ranges should I query to show ROI trends by platform?"
Ask for clarification when needed: If the AI's suggested query structure seems complex, ask it to explain why each component is necessary. You'll learn more about data analysis in the process.

Working with Complex Queries

For sophisticated analysis involving multiple data sources or calculations, MCP becomes especially valuable:

Multi-step query building

Start by asking: "I want to calculate customer acquisition cost by channel. What queries do I need to run?"

The AI can break this down into:

First query: Pull ad spend by channel
Second query: Pull conversion data by channel
Calculation: Explain how to combine the results

Formula generation

Ask: "What formula should I use to calculate ROAS in Dataslayer?"

The AI explains: (Revenue / Ad Spend) * 100 and tells you which specific metric fields to use from your data sources.

Advanced filtering logic

Request: "How do I structure filters to show only campaigns that spent more than $500 and had conversion rates below 2%?"

The AI provides the exact filter conditions and logic operators to apply.

Why This Matters

Even though you're implementing queries manually in Dataslayer, having MCP generate the query structure provides significant value:

Time savings: Skip the trial-and-error of figuring out which metrics and dimensions to use. Get the right structure immediately.
Learn as you go: Each query specification teaches you more about data analysis and best practices for structuring marketing queries.
Avoid mistakes: Reduce errors from incorrect metric selections, wrong date range comparisons, or missing filters.
Handle complexity: Tackle sophisticated multi-platform analyses that would be intimidating to structure from scratch.
Consistency: Generate standardized query structures for recurring reports, ensuring consistency across time periods.

What Happens Next?

Once you have your AI-generated query specification:

Copy the structure: Take note of all metrics, dimensions, filters, and date ranges the AI recommended.
Open Dataslayer: Navigate to your Dataslayer workspace and start building the query manually.
Apply each component: Select the metrics, add the dimensions, configure the filters, and set the date range exactly as specified.
Run and verify: Execute the query and verify the results match what you expected. If adjustments are needed, ask the AI to refine the query structure.
Save for reuse: Once you've validated the query works, save it for future use or recurring reports.

Works with All AI Providers

Query generation works consistently across all three AI providers that support Dataslayer MCP:

ChatGPT: Great for quick query suggestions and explaining why specific metrics are recommended
Claude: Excellent for complex, multi-step query planning with detailed explanations
Mistral AI: Solid query structuring with privacy-focused EU infrastructure

Choose whichever provider you're most comfortable with, they all understand how to help structure data queries effectively.