Validating Your BC MCP Setup with Real-World Queries

Introduction

Your BcMCPProxy is built, Claude Desktop is configured, and the BC MCP Server is live. Now let’s put it all to the test.

In this final post, we go beyond simple data lookups. The real power of connecting an AI agent to Business Central is not just reading records. It is asking business questions in plain English and getting visual, actionable answers. We will walk through two scenarios that demonstrate exactly that, followed by best practices for working with AI agents.

Scenario 1: Project Budget vs. Actual Analysis

This is where things get interesting. Instead of opening BC, navigating to project ledger entries, exporting to Excel, and building a chart manually, you just ask.

Prompt

"I want to see budget vs actual for all Projects

with a nice graphic view"

What Happens Behind the Scenes

Claude does not just make one API call. It thinks through the problem and executes multiple steps:

1. Claude identifies that it needs project data with budget and actual figures. It calls the BC MCP Server to read the Project API page.

2. It retrieves budget amounts and actual costs for all projects. If the data spans multiple API pages (project tasks, project planning lines, project ledger entries), Claude calls each one as needed.

3. Claude then generates an interactive chart, typically a grouped bar chart, comparing budget vs. actual for each project side by side.

4. The chart is rendered as a visual artifact directly inside Claude Desktop. No Excel export. No copy-paste. Just ask and see.

✅ Setup Note

For this scenario, I created a custom API page in Business Central that exposes the Job Planning Lines table with all the relevant budget and actual fields. The standard BC APIs do not include project planning data out of the box, so a custom API page was needed to give the agent access to this information. This is a common pattern: if the data you want is not available through standard APIs, build a custom API page, add it to your MCP configuration, and the agent can use it immediately.

Why This Matters

Think about how long this takes the traditional way. Open BC, navigate to the right page, apply filters, export to Excel, format the data, build a pivot chart, adjust colors and labels. That is 15 to 30 minutes of work, depending on how many projects you have.

With MCP, you type one sentence and get a visual answer in seconds. The agent handles all the API calls, data shaping, and visualization for you.

Expected result: A bar chart showing budget vs. actual cost for every project in your BC environment along with total budget, actual cost, remaining budget and budget utilization.

✅ Pro Tip

You can refine the prompt to focus on specific projects or add more detail. For example: "Show me budget vs actual for projects J00010 and J00030 with a breakdown by project task." The more specific your prompt, the more targeted the output.

✅ Prompt Refinement

If the initial chart does not look quite right, just tell Claude what to change. "Make it a horizontal bar chart instead" or "Add percentage variance labels" or "Only show projects that are over budget." The agent iterates on the visualization without you touching any code.

Scenario 2: Available Inventory Overview

Inventory visibility is one of those things that everyone needs but nobody wants to build reports for. Let’s skip the report.

Prompt

"Show me available inventory in a nice chart"

What Happens Behind the Scenes

1. Claude calls the BC MCP Server to read the Item API page and retrieves all items with their inventory quantities.

2. It filters for items that actually have inventory on hand (skipping zero-stock items to keep the chart clean).

3. Claude generates a visual chart. Depending on the number of items, it might use a bar chart for a smaller catalog or a treemap/bubble chart for a larger one.

4. The chart is rendered as an interactive artifact in Claude Desktop, with item numbers, descriptions, and quantities.

Why This Matters

This is a question that gets asked in every warehouse meeting, every sales call, and every planning session. Instead of logging into BC, navigating to the item list, sorting by inventory, and maybe exporting it for a visual, you just ask.

And because it is conversational, you can immediately follow up: "Which items are below their reorder point?" or "Show me just the items in the FINISHED category" or "What is the total inventory value?" The agent keeps the context and builds on the previous answer.

Expected result: A visual chart showing available inventory quantities across your items. Items with the highest stock levels are immediately visible. Zero-stock items are excluded for clarity.

✅ Taking It Further

Try chaining requests: "Show me available inventory, then highlight items that have not had any sales in the last 90 days." Claude can cross-reference inventory data with sales data across multiple API calls to surface slow-moving stock. This kind of ad-hoc analysis used to require a dedicated report. Now it is a conversation.

The Bigger Picture

These two scenarios are just the beginning. The pattern is always the same: ask a business question in plain English, and the AI agent figures out which BC API pages to call, fetches the data, and presents it visually.

Here are a few more ideas to try once your setup is working:

• "Show me a trend of sales order totals by month for the last 12 months."

• "Which customers have overdue balances? Show me a breakdown by aging bucket."

• "Compare actual vs. budget for GL accounts in the 6000 range this quarter."

• "What are our top 10 selling items by revenue this year? Show me a chart."

Each of these would traditionally require navigation, filtering, exporting, and charting. With MCP, they are one-line prompts.

Best Practices

Security

• Least privilege: Start with a read-only MCP configuration. Only enable write operations after thorough testing.

• No secrets in config: No passwords or secrets are stored. The proxy uses Azure’s delegated auth flow.

• Separate configurations: Create purpose-specific configurations (e.g., Finance-ReadOnly, Sales-ReadWrite).

• Audit access: Review who has the MCP - ADMIN permission set in BC.

Working with Agents

• Be precise: Agents work best with specific, clear requests. “Show me customers” is good; “Show me the top 10 customers sorted by balance” is better.

• Request complete data: When you need all records, say so explicitly and instruct the agent not to use the top parameter.

• Understand tool calls: Each MCP tool call is a separate API request. Complex queries may take a few seconds.

• Review before approving: For sensitive operations, the agent will ask for confirmation. This is by design.

Maintenance

• Stay current: The BC MCP Server is in public preview and may change. Watch the Microsoft Learn documentation for updates.

• Expand over time: As BC evolves, new API pages may become available. Add them to your MCP configuration to expand agent capabilities.

• Test after updates: If something breaks after a BC update, re-check Feature Management and MCP configuration.

Conclusion

Over these four posts, we have gone from understanding what AI agents and MCP are, through enabling the BC MCP Server and configuring it, to building a proxy executable and testing it with real business questions.

The key takeaway is this: the value is not in reading individual records. It is in asking business questions and getting visual, actionable answers in seconds. Budget vs. actual comparisons, inventory overviews, trend analysis. All of this is now a conversation, not a report-building exercise.

The BcMCPProxy is a temporary bridge. Microsoft will likely provide direct MCP client support in future releases. But it works today and demonstrates the enormous potential of agentic AI in the Business Central ecosystem.

The future is agentic. Let’s build it.