Building Paginated Reports Faster with the Power BI MCP Server

Paginated reports in Power BI are powerful, but they can also be tedious to build. Anyone who has worked in Report Builder knows the process: writing queries, configuring parameters, testing datasets, adjusting layouts, and repeating the cycle until everything renders correctly.

Recently, I experimented with a different approach: using the **Power BI Remote MCP server** to generate a draft paginated report.

Instead of starting in Report Builder and manually building the report piece by piece, I provided MCP with the context it needed — the semantic model, a template file, and some examples. From there, it generated a new paginated report file that closely resembled the final design.

The result was a much faster way to get a report off the ground before moving into refinement and formatting.

Here’s how the workflow looked.

The Challenge with Paginated Reports

Paginated reports are still essential for operational reporting, financial statements, and exports that require precise formatting. But the development workflow can often slow teams down.

Typical steps include:

• Identifying the correct tables and measures in the semantic model
• Writing DAX queries that return the exact fields needed
• Configuring parameters and filters
• Building table structures and grouping logic
• Adjusting layouts and formatting

In many cases, data exploration and report design happen at the same time, which leads to multiple cycles of editing both the dataset and the layout.

I wanted to see if MCP could help move some of that work earlier in the process.

Using MCP to Explore the Semantic Model

The first step was providing the semantic model ID to the Power BI remote MCP server. This allowed MCP to connect to the correct dataset and understand the structure of the model.

Once connected, MCP could inspect elements of the semantic model, such as:

• Available tables
• Columns and measures
• Relationships between tables

Instead of manually searching through fields in Report Builder, I could use MCP to explore the dataset and understand what fields would be needed for the report.

This made it easier to identify the dataset requirements before building the report itself.

Providing Context: Template, Query, and Example

To help MCP generate the report, I provided several pieces of context.

First, I gave it a paginated report template file that we typically use internally. The template already includes standard formatting, page settings, and layout conventions.

Next, I provided a sample DAX query so MCP could understand the shape of the dataset and how the data would be pulled from the semantic model.

Finally, when MCP struggled to produce the exact structure I needed, I provided an example of a working paginated report that was similar to the one I was building. This gave MCP a reference for how the dataset and layout should work together.

Providing these examples dramatically improved the quality of the output.

Example MCP Prompt Used to Generate the Report

To generate the draft report, I provided MCP with explicit instructions describing how to create the report file, which template to use, and what dataset to include.

The key idea was to anchor the report to an existing template and provide the dataset query directly.

Below is a simplified version of the prompt I used:

Create a new paginated report in this repository by copying the template file.

Template: <template file path>
New report: <new report file path>
Working report: <working report file path>

In the new report:

1. Define the following report parameters:
   - OrgNodePk (Integer) – required
   - PeriodPk (Integer) – required

2. Create or update the dataset named "dsRevenueFee".

3. Configure the dataset to use the following DAX query exactly:

<Insert DAX query here>

After running this prompt, MCP generated the report file and summarized the changes it made to the RDL

This prompt instructed MCP to:

• Copy the existing report template
• Generate a new report file
• Define required report parameters
• Configure the dataset using the specified query

Because the template already included formatting and layout conventions, MCP was able to generate a report file that followed our standard structure.

When I opened the generated report in Report Builder, the dataset and parameters were already configured, and the report closely resembled the layout I was aiming for.

At that point, the remaining work was simply refining the layout and validating the data.

Generating the Report Draft

With the semantic model, template, query, and example in place, MCP was able to generate a new paginated report file based on the template.

Below is the generated report opened in Report Builder. The table structure, grouping, and dataset bindings were already created by MCP.

Instead of simply returning a query or instructions, MCP produced a report that already included:

• The dataset definition
• The core query logic
• Table structures and grouping
• Formatting consistent with the template

When I opened the generated file in Report Builder, the report already resembled the structure I wanted.

At that point, the work shifted from building the report from scratch to refining and validating the report that MCP had created.

Moving into Report Builder

Once the draft report was generated, I opened it in Report Builder to finalize the details.

The remaining work focused on:

• Validating the dataset results
• Refining grouping or expressions
• Adjusting layout and formatting
• Verifying totals and parameters

Because the skeleton of the report was already in place, the development process moved much faster than starting from a blank report.

Where MCP Helped the Most

The biggest advantages came from three areas.

Faster Model Discovery

Instead of manually exploring the semantic model inside Report Builder, MCP helped quickly identify the fields needed for the report.

Automated Report Scaffolding

By using the template and examples, MCP generated the initial report structure — including the dataset and layout — which significantly reduced the amount of setup work.

Reduced Iteration in Report Builder

Because the report already existed in draft form, I spent less time going back and forth between dataset development and layout design.

Limitations

This workflow does not replace Report Builder entirely. There are still tasks that MCP cannot handle directly, including:

• Fine-tuning layout and formatting
• Complex expressions or calculations
• Final validation of report logic

However, it can dramatically reduce the time required to build the initial report structure.

Final Thoughts

Paginated reports remain a key part of operational reporting, especially when precise formatting and exports are required.

Using the Power BI MCP server to generate a draft report from a template, semantic model, and example report can significantly speed up the early stages of development.

Instead of starting from scratch in Report Builder, MCP can create a strong starting point that developers can refine and validate.

For teams that build paginated reports regularly, this approach can turn MCP into a practical development tool rather than just an experimental AI feature.