At a Glance A client in Abu Dhabi discovered more than 400 posted G/L entries with incorrect dimension values. Using Dimension Correction in Microsoft Dynamics 365 Business Central, the finance team was able to correct the dimension values in bulk without creating reversal or adjustment entries. The corrections could be validated, audited, reviewed, and undone, providing a controlled approach to fixing posted transactions. However, it is important to note that Dimension Correction applies to G/L entries only; related sub-ledger entries retain their original dimensions. Table of Contents Introduction Why Dimension Errors Happen in the First Place What Dimension Correction Actually Does Setting Up Before the First Correction Step 1: Starting the Correction from the Ledger Step 2: Selecting Entries in Bulk Step 3: Defining the Dimension Changes Step 4: Validating Before You Run Step 5: Running the Correction Step 6: Reviewing, Undoing and Auditing A Note on Cost Accounting The Outcome Best Practices We Recommend Conclusion Frequently Asked Questions About the Author Introduction Dimensions are what turn a general ledger from a list of numbers into something management can actually use. A single expense posting tells you how much was spent; the dimensions on that posting tell you which department spent it, on which project, at which location. When dimensions are right, reports by cost centre, business unit or project take seconds to produce. When they are wrong, every one of those reports quietly becomes unreliable. That was the situation one of our clients in Abu Dhabi found themselves in. During routine month-end review, their finance team noticed that a large group of posted transactions carried the wrong dimension values. Some entries were tagged to the wrong department, others to the wrong project, and a few had no value at all where one was expected. By the time the pattern was traced, more than 400 general ledger entries were affected. Reversing and re-posting each transaction was not an option. It would have doubled the number of entries in the ledger, cluttered the audit trail and taken days of effort. Editing entries one at a time was equally impractical. Instead, we used the Dimension Correction feature in Microsoft Dynamics 365 Business Central to fix all of them in a controlled, bulk operation, without touching the amounts or the accounts. This blog walks through how the feature works, how we applied it for this client, and what to keep in mind before you run a correction in your own environment. Why Dimension Errors Happen in the First Place In our experience, incorrect dimensions rarely come from a single cause. For this client, the errors came from a mix of everyday situations that most finance teams will recognize: 1. Default dimensions not updated after an internal restructuring, so new transactions kept inheriting the old department code. 2. Manual journal entries where users picked a similar-looking value from the lookup list. 3. Recurring journals that were copied forward month after month with an outdated project code. 4. Imported data from a spreadsheet where the dimension column was shifted by one row. None of these errors affected the trial balance. The debits and credits were correct and the accounts were correct. What was wrong was the analytical layer on top, which meant that department-wise P&L and project cost reports no longer matched reality. What Dimension Correction Actually Does Dimension Correction lets an authorized user change the dimension values on general ledger entries that have already been posted. It does this without creating new journal lines, reversals or adjustment postings. The amounts, dates, document numbers and G/L accounts stay exactly as they were; only the dimension set linked to each entry is updated. Every correction is recorded as its own document with a description, a status and a list of the entries it touched. That means auditors and finance managers can always see what was changed, when, and why. Important limitation The correction applies to G/L entries only. Related sub-ledger entries, such as customer, vendor, item or fixed asset ledger entries, keep their original dimensions. Since the purpose of the feature is accurate financial reporting, this is by design, but it is worth communicating to anyone who reports from sub-ledgers. Setting Up Before the First Correction Before our client’s finance team ran anything, we put two controls in place. 1. Decide who can correct dimensions Access to the feature is governed by the D365 DIM CORRECTION permission set. We assigned it only to the finance controller and one senior accountant. Users with this permission can create, run and undo corrections, so it should not be handed out broadly. 2. Protect dimensions that should never change On the Dimension Correction Settings page, you can list dimensions that are blocked from correction. For this client, we blocked the dimension used for statutory entity reporting, since any change there needed a formal adjustment rather than a re-tag. Step 1: Starting the Correction from the Ledger A correction can be started from two places: 1. General Ledger Entries page, using the Correct Dimensions action. This is the most flexible starting point when the affected entries are spread across many postings. 2. G/L Registers page, by selecting a register and choosing Correct Dimensions. This pre-loads all entries from that register and is useful when a single batch posting went wrong. Because the client’s 400+ entries came from different journals and dates, we started from the General Ledger Entries page. The first thing we filled in was the Description field, with a note explaining the reason for the change and a reference to the internal approval. It takes ten seconds and saves a lot of questions six months later. General Ledger Entries page showing the Correct Dimensions action. Step 2: Selecting Entries in Bulk This is where the feature really earns its place. Instead of picking entries one by one, the Selected Ledger Entries FastTab offers several ways to build the list: Option How we used it Add by Filter Filtered on G/L account range and posting date range to pull … Continue reading How an Abu Dhabi-Based Diversified Holding Company Corrected Mis-Tagged Ledger Entries in Dynamics 365 Business Central →

Summary This solution is being implemented initially for the Chart of Accounts of an Emirates-based firm , where significant updates are required across the existing G/L Account master. The objective is to provide Finance users with a simpler and more familiar way to review and maintain supported records through Excel, reducing the dependency on technical users for routine maintenance activities. Although the current implementation focuses on the Chart of Accounts, the approach is not limited to G/L Accounts and can be extended to other supported master data, such as Customers, Vendors, Items, and similar records. By providing an Excel-based alternative for suitable maintenance scenarios, the solution reduces the need to use Configuration Packages for every routine update while allowing technical teams to focus on complex development, integrations, automation, and customization activities. Table of Contents Introduction Understanding the Business Requirement The Traditional Approach The Business Problem The Excel-Based Solution Using Excel Integration Selecting the Required G/L Accounts Reviewing Existing Data Updating Supported Fields Publishing Changes Verifying the Changes Implementation Approach Moving Bulk Maintenance Toward Functional Users Business Impact Important Considerations Frequently Asked Questions Conclusion Introduction The Chart of Accounts is one of the core components of financial management in Microsoft Dynamics 365 Business Central. As part of the current implementation for an Emirates-based firm, significant updates are required to the existing Chart of Accounts to align the financial structure with the organization’s evolving reporting, accounting, and operational requirements. Since the required changes involve a large number of G/L Accounts, updating each account individually within Business Central would be repetitive and time-consuming. The volume of changes creates a requirement for a more efficient approach that allows multiple existing records to be reviewed and updated in a structured manner. Traditionally, Configuration Packages can be used to perform bulk data updates in Business Central. While this provides a powerful mechanism for data migration and bulk maintenance, routine updates may still require technical involvement and additional configuration steps. For the current Chart of Accounts requirement, the objective is to provide Finance users with a simpler and more familiar method for making supported bulk changes. The Key Question: Can Finance users efficiently review and update a large number of G/L Accounts through a familiar Excel-based interface, while reducing the need for technical assistance for routine bulk maintenance activities? To address this requirement, the proposed approach uses the Microsoft Dynamics 365 Business Central Excel integration for supported bulk maintenance activities. Although the initial implementation is focused on the Chart of Accounts due to the significant updates required for the Emirates-based firm, the same approach can also be applied to other supported master data, such as Customers, Vendors, Items, and other relevant records, where similar bulk maintenance requirements arise. Understanding the Business Requirement During the implementation, the consultant was faced with a practical challenge: the Finance team needed to make a significant number of changes to the existing Chart of Accounts, but updating each G/L Account individually in Business Central would require considerable manual effort. The consultant also needed a solution that Finance users could work with directly, without having to depend on the technical team for every routine change. The Excel integration provided a way to address this issue by allowing the consultant to work with the relevant G/L Account data in a familiar spreadsheet environment. Instead of opening and modifying accounts one by one, the required records could be brought into Excel, reviewed together, and the necessary values could be prepared or updated in bulk. This made the large-scale Chart of Accounts maintenance activity more manageable and significantly reduced repetitive manual work. How the Requirement Was Resolved: The consultant can use the Business Central Excel integration to provide Finance users with a structured way to work with supported G/L Account data in bulk. This allows multiple records to be reviewed and updated through Excel rather than requiring individual changes within Business Central for every account. This approach also addressed the dependency between the Finance and technical teams. Finance users can handle suitable routine master-data maintenance themselves, while the technical team remains available for activities that genuinely require technical expertise, such as complex customizations, integrations, automation, troubleshooting, and development. Although the immediate problem was related to the Chart of Accounts, the solution is not limited to G/L Accounts. The same concept can be applied wherever Business Central supports the relevant Excel-based maintenance capability, including other master data such as Customers, Vendors, Items, and similar records. This makes the approach useful beyond the immediate implementation and provides a repeatable method for handling future bulk maintenance requirements. The Traditional Approach Before considering the Excel-based approach, the consultant could use Configuration Packages to perform bulk updates to the Chart of Accounts. Configuration Packages are a standard and powerful Business Central capability for importing, exporting, and managing data across Business Central tables and remain particularly useful for data migration, initial configuration, and structured data-import scenarios. However, for the current requirement, the Finance team needed to make a significant number of changes to existing G/L Accounts. Using a Configuration Package for every such change could require additional preparation and involvement from a technical or experienced Business Central user. This created an additional dependency for what was essentially a routine master-data maintenance activity. The consultant therefore had to consider not only how the data could be updated in bulk, but also how the process could be made easier for the Finance team while maintaining appropriate control over the data. Finance Identifies Changes → Prepare Update Data → Technical Assistance → Configure Package → Import & Validate → Finance Verification This approach works well when there is a genuine requirement for structured data migration or when multiple related tables and fields need to be handled together. However, for repetitive updates to existing master records, it can result in additional steps that are not always necessary for the Finance user’s immediate requirement. The objective, therefore, was not to replace Configuration Packages altogether. Instead, the consultant identified an opportunity to use the Business Central Excel … Continue reading How an Abu Dhabi-Based Diversified Holding Company Simplified Master Data Maintenance in Dynamics 365 Business Central Using Excel →

Summary This post started with a real request. A Netherlands-based sustainability certification company that runs its certification programme on Dynamics 365 wanted the certificates it issues to come straight out of the CRM, instead of being rebuilt by hand in a document every time. The problem itself is common. Certification bodies, testing labs and manufacturers issue certificates every day, and in most organisations that still means a Word template, a lot of copy and paste, and a PDF sent by email. The data that belongs on the certificate already lives in Dynamics 365, so why can’t the certificate come straight from the record? In this blog we build exactly that. A Generate Certificate button on a Dataverse form calls a Custom API, a small plugin hands the data to an Azure Function, and a branded, print-ready PDF comes back in about four seconds. Today the PDF is stored on the record as a base64 string, previewed and downloaded right on the form. Because the renderer only returns bytes, sending the same file to SharePoint, Azure Blob Storage or an email is a change to one step, not a rebuild. In This Blog This blog walks through a complete, working certificate generator for Dynamics 365, from the data model to the button, using a product certification scenario. The Scenario: A certification body issues product certificates and needs every one to be accurate, branded and traceable. The Approach: Keep the data in Dataverse, render the PDF in an Azure Function, and connect them with a Custom API. The Action: One click on the form produces the certificate, stores it on the record and shows a live preview. The Outcome: No manual formatting, no retyping, no per-document licence fees, and a design that can later publish to SharePoint or Blob storage. Table of Contents Summary→ In This Blog→ Business Challenges→ Solution Overview→ Technical Approach→ Impact→ Conclusion→ FAQ→ Get in Touch→ Business Challenges A certificate is a legal statement: this product, made by this company, meets this standard, from this date to that date. When it is produced by hand, the risks are small individually and expensive together. Copy and paste errors. Product names, model numbers and expiry dates are retyped from the CRM into a document. One wrong digit on an issued certificate means a reissue and an awkward conversation. Template drift. Every coordinator keeps a slightly different copy of the Word template, so logos, wording and signatories quietly diverge. No trail. Once the PDF leaves someone’s desktop there is no record of which version was produced, when, or from which data. Platform limits. Dataverse plugins run in a sandbox that cannot lay out and paginate a PDF, and a JavaScript-only solution would expose service keys to every user’s browser. Before Word template and email Values retyped from the record Layout depends on who made it File lives in someone’s Downloads folder No link back to the CRM record After One click on the record Values read directly from Dataverse One design, driven by configuration PDF stored on the record with its hash Preview and download on the same form Solution Overview The demo uses a fictional certification body, Contoso Product Assurance, which certifies products made by manufacturer accounts against its own published standards. The whole flow runs from a single button: FormGenerate CertificateCommand bar button on the Product Certificate form DataverseCustom APIBound action cf_GenerateCertificate PluginCollect the dataCertificate, product, holder and standard AzureRender the PDFFunction returns the file as base64 RecordStore and previewBase64 saved, previewed and downloadable Architecture One click on the form, one PDF back on the record. The dashed box is where SharePoint, Blob storage or email plug in later. Data modelThree custom tables (Standard, Product, Certificate) plus the standard Account table for manufacturers. Custom API and pluginOne server-side entry point. The function key never reaches the browser. Azure FunctionA .NET 10 renderer on the Consumption plan. Vector PDF, embedded fonts, QR code. Form experienceGenerate and Download buttons plus an inline PDF preview panel. A certificate generated from Dataverse data. Every value on it comes from the record; nothing is typed. Technical Approach 1. The data model Everything lives in one solution, CFCertificateGeneration, so it moves between environments as a unit. Table Purpose Key columns Certification Standard What products are assessed against Code, Version, Default Scope, Validity (Months) Certified Product What is being certified Product Number, Manufacturer (Account), Category Product Certificate The issued certificate Auto-numbered Certificate Number, Status, Issue and Expiry Date, Product, Standard, Holder, Signatory, PDF (Base64), File Name, SHA-256, Generated On The Product Certification app: every certificate with its status, validity and when it was last generated. The certificate number is a Dataverse auto-number column with the format CPA-{DATETIMEUTC:yyyy}-{SEQNUM:5}, so numbering is guaranteed unique by the platform rather than by a person. The certificate holder defaults to the product’s manufacturer and can be overridden when a brand owner holds the certificate for a product someone else makes. 2. The renderer The PDF is drawn with SkiaSharp and the QR code with QRCoder, both open source. The same drawing code targets either a PDF page or a PNG bitmap, which means the image you check during development is exactly what the customer receives. Text is real, selectable text in embedded fonts, every graphic is a vector shape rather than an image, and long product names shrink to fit instead of overflowing the frame. CertificateRenderer.csC# public RenderedCertificate RenderPdf(CertificateRequest request) { Validate(request); using var output = new MemoryStream(); using (var document = SKDocument.CreatePdf(output, metadata)) { var canvas = document.BeginPage(842f, 595f); // A4 landscape, in points Draw(canvas, request); // same code paints the PNG preview document.EndPage(); document.Close(); } return new RenderedCertificate(output.ToArray(), FileNameFor(request.CertificateNumber)); } Vector from edge to edge, so quality never breaks There is not a single raster image in the certificate. Every line of text is real text in an embedded font, and the frame, the seal, the dividers and even the QR code are drawn as vector shapes. Zoom to 800%, print it on A3 or project it on a … Continue reading How a Sustainability Certification Nonprofit Automated Certificate Generation in Dynamics 365 →

Summary A home loan bank in the Maldives came to us needing legal, customer-facing reports for loan statements and overdue notices that had to reflect exact facility figures while matching the bank’s letterhead and legal wording. Traditional RDLC-based report development would have created significant IT dependency and delay for a document like this. Instead, our team used Word-based report layouts in Microsoft Dynamics 365 Business Central, which let us design the legal notice in a familiar document tool while Business Central handled populating it with live facility and customer data. We mapped the bank’s Business Central fields into a Word template, tested it against real facility scenarios, and delivered a working legal report in approximately two days, which is a turnaround that would traditionally take weeks with RDLC. This article walks through how we approached the build, using a sanitized sample report in place of the client’s confidential documents, and explains when we’d recommend Word over RDLC for a bank’s reporting needs. Table of Contents Introduction The Problem: IT Bottlenecks and Delayed Customer Documents The Word-Based Reporting Solution 3.1 How the Word-Based Approach Works 3.2 Creating the Word Template 3.3 Mapping Business Central Fields 3.4 Testing and Delivering the Report Sample Walkthrough: The Legal Report We Built Word Report vs. RDLC Report Time and Effort Comparison Why Banking Teams Benefit from Word Reports What Types of Banking Documents Work Best? Business Benefits When RDLC Is Still the Better Choice FAQs Conclusion About the Author Introduction Legal, customer-facing documents are one of the most sensitive parts of any lending business. A home loan bank in the Maldives came to us needing exactly this kind of document: a legal report covering a loan statement and overdue notice, which had to reflect exact facility figures, carry the correct legal clauses, and match the bank’s letterhead, all while pulling live data straight out of Business Central. Producing a document like this inside Microsoft Dynamics 365 Business Central can easily turn into a multi-week project if it’s built the traditional way. RDLC-based reporting usually means a developer has to build and maintain the report layout, write or modify code, configure data sources, and run several rounds of testing before the document is ready to go out to a customer. For a fairly standard legal document, like a loan statement or overdue notice, that kind of timeline is hard to justify. So instead of taking the client down the RDLC route, we proposed another option: Word-based report layouts. Word templates let us design the layout of the legal report in a tool the bank’s own team could later maintain, while Business Central handled populating it with the correct facility and customer data. The key idea: We designed the legal report’s layout and wording in Word, and let Business Central handle pulling in the correct facility data, so the client wasn’t locked into a developer for every future change. This article walks through how we built it, using a sanitized sample report in place of the client’s confidential documents, and explains how a report that would traditionally take weeks of RDLC development was designed, mapped, and delivered in approximately two days using a Word-based approach. The Problem: IT Bottlenecks and Delayed Customer Documents When the client first came to us, their existing experience with reporting was the same one many banks run into: a dependency between operations teams and technical teams that turns a simple document request into a long wait. The kind of request that used to happen internally looked something like this: “Hey, can we get a loan statement that shows the customer’s outstanding EMI, fines, and facility balance in one clean document?” “Sure, I’ll put in a ticket with IT.” [Two weeks later…] “The statement is almost done. It should be ready in another couple of weeks.” [Four weeks later…] “It’s finally here. Wait… the layout doesn’t match our branch letterhead format.” This kind of situation can happen when a relatively standard customer document becomes dependent on development resources. Why Do These Delays Happen? RDLC report layouts can require specialized development knowledge. Developers need time to configure data sources and implement the required reporting logic. Testing and troubleshooting can add additional development cycles. Small formatting changes such as a different overdue clause or a revised disclaimer may require technical assistance. Future modifications can place the document back into the development queue. Branch or collections staff may resort to manual, Word- or Excel-based workarounds while waiting for the official document. The result is a disconnect between the people who understand exactly what a legal notice or statement needs to say and the people responsible for implementing it in the system. The question we asked the client: What if your own operations or collections team could design and maintain these legal documents, using a tool they already know instead of relying on a developer for every change? The Word-Based Reporting Solution Microsoft Word was already familiar to the bank’s operations staff, so we proposed taking advantage of that. Business Central supports using Word templates directly as report layouts, which meant we didn’t need to build the legal report’s presentation through RDLC at all. Instead of developing the entire document presentation through RDLC, we designed the layout directly in Word and mapped Business Central’s available facility and customer data to the appropriate fields within that template. The solution: We used Word for the legal report’s presentation and Business Central for the facility data behind it. This separated document design from complex layout development and gave the client’s operations team greater control over formatting, tone, and compliance wording going forward. How We Approached the Build Our overall process was straightforward: Design the legal report layout using Microsoft Word. Add the required data fields to the Word template. Map those fields to the appropriate Business Central data. Run the report and let Business Central populate the template. Review the generated document with the client and adjust the layout where needed. The important distinction is that the document’s visual … Continue reading How We Built Legal Reports in Word on Business Central for a Home Loan Bank in the Maldives →

Summary This article explains how a data pipeline can be used to prepare Business Central data for demand forecasting. Raw data may contain missing values, inconsistent fields, and information that is not immediately suitable for analysis. The pipeline uses a three layer Medallion architecture: Bronze for storing the raw data, Silver for cleaning and preparing it, and Gold for creating the final data needed for analysis and forecasting. A key principle throughout the process is “NULL is not 0” . A missing value does not always mean that the value is zero. The pipeline therefore uses context aware handling and missing data flags so that the original meaning of the data is not lost. The article explains the complete flow from data ingestion through cleansing, NULL handling, transformation, and aggregation, along with the code and examples used to implement each step. Table of Contents 1. Introduction 2. The Business Problem 3. The Solution 3.1 Medallion Architecture Overview 3.2 Ingestion API from Business Central 3.3 The NULL Philosophy 3.4 Silver: Intelligent Imputation 3.5 Flagging the Unknown 3.6 Gold: Aggregating Without Lying 4. Limitations & Considerations 5. Business Impact 6. FAQs 7. Conclusion 1. Introduction In modern supply chain and demand forecasting, data quality is the foundation of every decision. Business Central can serve as a source for sales, inventory, purchases, and transfer data. However, raw CDC (Change Data Capture) payloads extracted through the OData API may require additional cleansing before they are forecast ready. Fields were missing, column names were inconsistent, and most critically NULLs were everywhere . While Business Central stores transactional data reliably, the OData API exports often include null values for optional fields, unpopulated attributes, or fields that simply don’t apply to certain record types. Treating these NULLs as zeros would have been a catastrophic mistake in demand forecasting. A NULL in a sales quantity column doesn’t mean “zero units sold” it means “we don’t know”. Erasing that distinction would hide the difference between genuinely zero demand and missing data, leading to under forecasting, misplaced safety stock, and costly stockouts. To solve this, I built a 3 layer Medallion pipeline (Bronze → Silver → Gold) in Azure Databricks. I also designed a custom ingestion API using Databricks’ OData V4 connector wizard to pull 12 ERP entities directly into Azure Blob. The Silver layer introduced a “NULL is not 0” philosophy, with context aware imputation and boolean flags to preserve data quality signals. The Gold layer aggregated daily demand per item location, leaving NULLs as NULL to correctly represent “no data” days. This article walks through the architecture, the imputation logic, and the practical lessons learned from transforming 12 messy datasets into a clean, forecast ready mart. 2. The Business Problem The organization relied on manual Excel spreadsheets to prepare demand forecasts across 22 high impact items and 3 warehouse hubs a total of 66 item location combinations . This manual process was error prone and unscalable. Several challenges emerged: Heavy reliance on spreadsheets: Demand planning was executed via disconnected, static Excel files. Frequent formula errors, broken references, and accidental overwrites led to incorrect estimates. Lack of scalability: Manually calculating forecasts across 66 combinations was time consuming. Excel could not dynamically model complex interactions like monthly seasonality and day of week demand patterns simultaneously. Reactive procurement and stockouts: Teams spent more time cleaning data than analyzing risk. Static buffer stocks failed to account for changing supplier lead times, resulting in emergency purchase orders or stockouts during peak seasons. NULLs were treated incorrectly: In many cases, missing values were simply replaced with 0, masking the true state of the data and causing the forecast model to underestimate demand. The Objective: Build a reliable, automated pipeline that ingests raw CDC data from Business Central, cleanses it with context aware logic, and produces a daily demand mart that distinguishes between “zero sales” and “unknown sales” . 3. The Solution The solution was designed as a Medallion architecture with three distinct layers, each serving a specific purpose in the data quality journey. 3.1 Medallion Architecture Overview The pipeline processes 12 raw JSON datasets from Business Central through Bronze, Silver, and Gold layers in Azure Databricks. Medallion Architecture Business Central Data Pipeline Business Central OData V4 / REST API 12 CDC datasets → Databricks OData V4 Connector Auth · Pagination · Parsing → Azure Blob Bronze (Raw JSON) Immutable landing ↓ 🥈 Silver Layer Cleaning · Typing · Flagging • Remove @odata.etag • Standardize columns · Cast types → 🥇 Gold Layer Aggregation · Business Logic • Daily demand per item location • NULLs preserved as “no data” ↓ 📊 Forecasting & Analytics Power BI · ML Models · Business Decision Support Read only pipeline: cleaned data remains in the analytical pipeline. Medallion architecture: Business Central OData → Bronze → Silver (cleaning) → Gold (aggregation) → Analytics 3.2 Ingestion API from Business Central The first step was to build a reliable ingestion layer. I used the OData V4 connector wizard in Databricks to set up an authenticated connection directly to Business Central’s REST API. This connector handled pagination, authentication (Azure AD OAuth 2.0 with Key Vault), and parsing of the OData response format (data wrapped inside a “value” array). I configured it to pull all 12 datasets that feed into the forecasting pipeline. Security: Zero hard coded credentials all secrets stored in Azure Key Vault. Endpoints: Pulled items , sales_lines , purchase_headers , item_ledger_entries , and 8 other entities. Landing: Raw JSON payloads were written to Azure Blob (Bronze) as immutable snapshots, preserving the original CDC state. 3.3 The NULL Philosophy Before writing a single line of PySpark, I defined a clear rule: NULL is not 0 . This principle guided every transformation in the Silver layer. In demand forecasting, a NULL in a sales quantity column means “we don’t know”. It could be due to a missing transaction, a CDC gap, or a field that doesn’t apply to that record type. Treating it as zero would artificially deflate demand, leading to under … Continue reading Building a 3-Layer Pipeline to Cleanse Business Central Data for Forecasting →

Site-Level Master Planning Summary Multi-plant manufacturers often run Planning Optimization as one global master plan that nets supply and demand across every site and warehouse together. That’s fine right up until each plant has its own planners, its own coverage rules, and its own idea of when on-hand stock should get consumed first. Microsoft’s “Plan for a single site or warehouse in Planning Optimization” feature (Feature Management, available from version 10.0.44) fixes this at the source: a master plan can now filter on stock dimensions (site, warehouse, batch), not just items, so the plan’s entire universe of supply, demand, and coverage data can be scoped to one location. We rolled this out for a North American truck manufacturer running several plants under a single legacy global plan. Splitting planning by site let each plant’s team run and own a plan tuned to what’s actually stocked and consumed there, without touching anyone else’s numbers. On this page 01The problem with one global plan 02What the feature actually changes 03Turning it on 04Building a plan filter on stock dimensions 05Two plants, two different plans 06Running the site-specific plan 07The catch: full filter only 08FAQs 09Conclusion The problem with one global plan A single master plan covering every plant is simple to set up and easy to explain: one run, one set of planned orders, one schedule. It’s also where the friction starts once a business runs more than one plant with different products, different suppliers, and different teams responsible for each. Planners at one plant end up looking at planned orders shaped in part by another plant’s on-hand corrections and forecast edits. A coverage code change made for one site’s parts can shift output for a site that has nothing to do with it. And because it’s one run, a data issue at any single plant (a bad forecast import, a stuck transaction) can delay the plan for every other plant waiting on the same run to finish. What the feature actually changes Master plans have always supported an item filter. What’s new is a Stock Dimensions table sitting alongside the Items table inside the plan filter, meaning a plan can now be scoped by Site, Warehouse, Batch number, or any other inventory dimension, not just by which items it considers. That distinction matters: this isn’t a coverage-code setting that changes how the plan computes numbers for items it already sees. It changes what the plan sees in the first place. The filter cascades to every planning input, including inventory transactions, forecasts, and item coverage settings like safety stock. Two plans with the same items can end up looking at entirely different supply and demand once their stock-dimension filters diverge. Turning it on The feature ships behind Feature Management, disabled by default: 1Open the Feature Management workspace and find Plan for a single site or warehouse in Planning Optimization. 2Enable it. No separate deployment step is needed; the new filter option appears immediately under Master Planning > Setup > Plans > Master Plans. Building a plan filter on stock dimensions With the feature enabled, scoping a plan to one plant is a setup task, not a code change: 1Open or create a master plan under Master Planning > Setup > Plans > Master Plans. 2Open Plan Filter. The Stock Dimensions table now appears alongside the Items table, and any inventory dimension is selectable as a filter field. 3Add the dimension that defines this plant’s scope (typically Site and Warehouse) and set the values that belong to it. Two plants, two different plans The client’s plants didn’t just need separate scopes; they needed separate policies. One plant runs standard assembly parts with no lot tracking; another runs batch-tracked, shelf-life-sensitive components that need on-hand stock consumed before anything else. The stock-dimension filter made both plans possible from the same master plan setup, configured independently: Plant A: Chassis Assembly Filter: Site = 1, Warehouse = WH-CHASSIS Includes on-hand stock and stock transactions No batch dimension; parts aren’t lot-tracked here Standard pegging behavior Plant B: Engine Components Filter: Site = 2, Warehouse = WH-ENGINE, plus Batch number Override Pegging Sequence enabled: on-hand consumed before other supply Shelf dates respected for batch-tracked, perishable-sensitive components Completely independent run schedule from Plant A Neither configuration affects the other. Plant B’s shelf-life logic never touches Plant A’s parts, and Plant A’s simpler setup isn’t forced to carry batch-level complexity it doesn’t need. “A single global plan optimizes for the business on paper. Separate site-level plans optimize for the planners who actually have to run them.” Running the site-specific plan From Master Planning > Run > Master Planning (or the Master Planning workspace), select the plan for the plant you want to run. The system generates planned orders only within that plan’s stock-dimension scope, and nothing from other sites enters the calculation. The standard item filter is still available at run time to narrow further, or it can be left blank to run every item within the plan’s scope. The catch: full filter only This is the detail that caught our planning team off guard the first time: stock-dimension scoping only works through the master plan’s own Plan Filter, not through the ad hoc runtime dialog filter. Open that runtime dialog and only the Items table is available; Site, Warehouse, and Batch simply aren’t selectable there. Practical implication: planners can’t improvise a one-off site-scoped run from the run dialog. Every site or warehouse that needs its own independent plan needs its own master plan record, configured ahead of time, not a flexible plan filtered differently run to run. FAQs 1Does this replace coverage groups or item coverage settings? No. Coverage codes still govern how the plan calculates replenishment for the items it sees. This feature governs what the plan sees in the first place: the stock-dimension filter applies before coverage logic runs. 2Can supply and demand for one item get double-counted across two site plans? Not if the filters are built to be mutually exclusive: different Site/Warehouse combinations with no … Continue reading How a Leading North American Commercial Vehicle Manufacturer Scoped Master Planning to a Single Site or Warehouse in Dynamics 365 Finance & Supply Chain Management →

Summary In Business Central, users may need to view relevant purchase invoice details directly from General Ledger Entries for better transaction visibility and traceability. This enhancement was implemented based on requirements identified across multiple Business Central implementations, including projects for a leading UAE-based consortium and a financial institution in the Maldives. The solution enables purchase invoice details to be automatically captured against relevant General Ledger Entries for new transactions, while also providing an option to update previously posted entries. This helps finance users quickly identify the related invoice information without having to navigate through multiple pages or documents. Related Case Studies: Emirates Consortium – Customer Success HDFC PLC – D365 Business Central Integrated with Loan Management Table of Contents Introduction Understanding the Business Requirement The Challenge with Standard G/L Entry Descriptions Displaying vs. Storing the Description Designing the Solution Updating Historical G/L Entries Maintaining the Description for New Entries Implementation Approach Business Impact Frequently Asked Questions Conclusion Introduction In day-to-day financial operations, users often need to trace General Ledger Entries back to the original purchase transactions for verification, reconciliation, and reporting purposes. However, the standard General Ledger view may not always provide enough contextual information to quickly identify the details of the related purchase invoice. Based on requirements identified across multiple Business Central implementations, including projects for a leading UAE-based consortium and a financial institution operating in the Maldives, an enhancement was introduced to make relevant purchase invoice details available directly within General Ledger Entries. The enhancement improves transaction visibility by capturing the relevant purchase invoice description against the General Ledger Entry. For newly posted transactions, the information can be populated automatically, while previously posted entries can be updated through a dedicated action. This approach reduces the need for users to navigate between General Ledger Entries and posted purchase invoices, making financial review and transaction tracing more convenient and efficient. Understanding the Business Requirement From a Finance user’s perspective, the requirement was straightforward: When a user reviews a G/L Entry, the Description should provide meaningful information about the transaction. The requirement needed to work for both existing historical entries and transactions posted after the customization was introduced. 1. Historical G/L Entries Business Central may already contain a significant number of posted G/L Entries. These historical records also needed to have the enhanced description populated. Updating these entries individually would not be practical. Therefore, the solution needed to provide a controlled way to update existing G/L Entries in bulk. 2. New G/L Entries For future transactions, the description should be populated automatically during the posting process so that users do not have to manually update the G/L Entry after posting. This resulted in two key requirements: Provide a mechanism to populate descriptions for historical G/L Entries. Automatically maintain the description for newly created G/L Entries. The Challenge with Standard G/L Entry Descriptions The first question during the design was whether the required description could simply be calculated when the G/L Entry page was opened. At the page level, a value can be calculated dynamically based on related information. This approach can be useful when the requirement is simply to display additional information to the user. However, there is an important distinction between displaying a calculated value and storing that value in the underlying G/L Entry record. A page-level calculated value does not automatically become part of the underlying G/L Entry data. Therefore, although a user may see the expected description on the page, the value may not be available in the same way for other processes that directly consume the G/L Entry data. Key Design Consideration The requirement was not only to display a meaningful description. The description needed to be persisted against the G/L Entry record. Displaying vs. Storing the Description This distinction became an important part of the solution design. 1. Page-Level Calculation A page-level calculation is useful when the requirement is simply: “Show this information to the user.” The value can be generated when the page is opened without modifying the underlying G/L Entry record. 2. Persisted G/L Entry Description For this requirement, the description needed to become part of the actual G/L Entry data. The solution therefore stores the generated description directly against the G/L Entry. G/L Entry → Determine Transaction Information → Generate Description → Store in G/L Entry This approach ensures that the description remains available after the page is closed and can be consumed by other Business Central processes that use the G/L Entry record. Designing the Solution The solution was designed around two scenarios: historical G/L Entries and newly created G/L Entries. 1. Historical G/L Entries A dedicated action is provided to populate the enhanced description for existing G/L Entries. This allows Finance users or authorized administrators to update historical records without having to process each entry individually. 2. Newly Created G/L Entries For newly posted transactions, the description is maintained as part of the G/L Entry creation process. This means that the enhancement does not depend on users remembering to manually update the description after every posting. Two-Part Approach Existing Entries: One-time controlled update. New Entries: Automatic description population during the posting process. Updating Historical G/L Entries Since the system may already contain historical G/L Entries, simply implementing the logic for future postings would leave existing records without the enhanced description. To address this, we introduced a one-time action that allows the required description to be generated and stored for historical entries. The process can be represented as: Existing G/L Entries → Run Update Action → Determine Required Information → Generate Description → Update G/L Entry This approach provides a practical way to bring historical records into the same structure used for newly posted transactions. Maintaining the Description for New Entries Updating historical records solves only one part of the requirement. The same description logic also needs to be applied to future G/L Entries. Therefore, the solution ensures that the description is generated and stored when the relevant G/L Entry is created. This avoids a situation where historical records have the … Continue reading Enhancing General Ledger Visibility with Purchase Invoice Details in Dynamics 365 Business Central for a Maldives Financial Institution and an Abu Dhabi-Based Diversified Holding Company →

Summary The sales team at commercial laundry and linen rental organization could open any record in Dynamics 365 and still could not answer how much of the open book sat in healthcare. Sector was never a field on a deal, it was four boolean flags on the product, three joins away from the quote a manager wanted to group. We replaced the standard sales dashboards with Custom Sales Dashboard that reads the Dataverse Web API directly, rolls sector up through the line items, and puts leads, opportunities, quotes and signed contracts under a single set of filters. Every tile, cell and chart segment opens the records behind it and exports to Excel with real numbers and real dates. Table of Contents Summary→ Business Challenges→ Solution Overview→ Technical Approach→ Impact→ Conclusion→ Get in touch→ Business Challenges Commercial laundry and linen rental organization runs multiple laundries, called plants in the CRM, and quotes work by the weight a customer sends each week. A deal is a weekly tonnage and a weekly dollar figure before it is anything else, which is why the standard sales charts had so little to say about it. The reporting asks were ordinary. The data model underneath them was not, and every ask broke on a different part of it. Sector is not stored on the lead, opportunity, quote or order. It is independent flags on the product record: healthcare, motel, garments, etc. A chart grouped on the quote has no column to group by A single product can carry more than one flag, so one quote can legitimately belong to two sectors. Grouping without a rule double counts the same dollars Total Amount reads 0.00 on every quote and every order in the org. All money sits in annualrevenue and weeklyrevenue, which the standard sales charts and rollups do not read Contracts are salesorder records. The out of the box contract table is empty, so anything pointed at it returns nothing at all The plant lookup laundry is populated on roughly six quotes in ten. The rest only know their laundry through the originating opportunity Orders copied from an accepted quote frequently carry no line items of their own, so a product level report built on order lines quietly loses them Managers wanted one choice of sector, sales person or plant to move leads, opportunities, quotes and contracts together. Standard dashboards filter each chart against its own entity Nobody could see the forward book: which signed contracts start in the next twenty weeks, and what weekly tonnage each start week brings ⚠The real blocker Every one of these needs a value computed from grandchild rows, from the quote down to its lines down to the product flags. The chart designer groups on fields that exist on the record being charted, and this one does not exist anywhere. Solution Overview We built one page that lives inside the CRM as a web resource. It opens like any other dashboard, signs the user in with the session they already have, and reads live records rather than a nightly copy. The page carries six tabs. Overview holds a pipeline matrix and the stage, sector, sales person and plant mix. Pipeline Activity plots leads and quotes created per week for the last fifteen weeks. Contracts Won shows the forward book by contract start week. Win and Loss compares closed won against closed lost by value and by count. Approvals and Activities measures how long contract, logistics and freight rate decisions take. Reports lists contracted and quoted stock at product line level. Three filters at the top, sector, sales person and plant, drive every chart, tile and table on every tab at once A fourth filter, customer, appears only on the Reports tab because it only narrows those two tables Clicking any chart segment, matrix cell or tile opens a drawer listing the records behind that exact number Each row in the drawer links to the real CRM form, and the whole selection exports to Excel with numbers as numbers and dates as dates ⓘWhy the drill through mattered more than the charts A sales manager who cannot see the eleven contracts behind a bar will not act on the bar. Registering the source records for every visual was the difference between a picture and a working tool. Technical Approach Where the page runs The dashboard is a single HTML web resource, customDashboard, shipped in the main solution. Running inside the model driven app means it inherits the user’s authentication and their record level security, so a sales person sees their own scope without us writing a line of security code. It resolves the org URL from parent.Xrm.Utility.getGlobalContext() and falls back to window.location.origin when it is opened outside a form, which is what makes local debugging possible. SourceDataverse Web API v9.2Live reads with the signed in user’s context Load14 parallel queriesOne Promise.all on page open IndexLookup maps and sector indexProduct flags resolved once, then cached RenderMatrix, tiles and Chart.jsRedrawn in memory on every filter change ActDrawer and XLSX exportRecords behind the number, typed for Excel Everything loads once. Accounts, factories, depots, leads, opportunities, quotes, orders, the three line item tables, products, freight rates, delivery point risk assessments and activities all come down in a single Promise.all, and every later interaction is a filter over arrays already in memory. Changing a filter costs nothing on the network. Rolling sector up from the product The rollup walks the line items of a quote, order or opportunity, reads the flags off each product, and weights each sector by line weight times quantity. The heaviest sector becomes the primary, which is what the breakdown charts group on so the totals stay additive. The full list is kept separately and is what the sector filter tests against, so a quote that touches healthcare and motel appears under both filters and is counted once in the donut. cf_customDashboard.html, sector rollupjavascript function rollupSectors(lines, productIdField) { const weight = {}; lines.forEach(l => { const sectors = PRODUCT_SECTORS[String(l[productIdField] || ”).toLowerCase()] … Continue reading How an Australia-Based Commercial Laundry Company Built a Custom Dynamics 365 Sales Dashboard Beyond Standard Reporting →

Summary Return Purchase Orders affect operational health and are often overlooked in implementation planning. This guide covers the return workflow in Dynamics 365 F&O: creating return orders, executing warehouse movements, managing supplier shipments, and reconciling credits to accounts payable. We address configuration practices, warehouse operations, challenges (including trade agreement alignment), reporting frameworks, and integration patterns for multi-system environments. Table of Contents 1. The Problem: Why Return Purchase Orders Matter 2. The Core Flow: Return Purchase Order in D365 F&O 3. Warehouse Return Movement 4. Linking Movements to Returns 5. Configuration Deep Dive 6. Trade Agreement Alignment After Returns 7. Warehouse Practices 8. Configuration Checklist 9. Issues & Resolutions 10. Integration Considerations 11. Reporting & Monitoring 12. FAQs 1. The Problem: Why Return Purchase Orders Matter In any supply chain, orders don’t always go as planned. Quality issues, over-shipments, damage, and spec mismatches happen. When they do, the warehouse receives material that doesn’t meet requirements—and the system needs to reflect that. A Return Purchase Order is not just a procurement transaction. It’s an operation that touches procurement, inventory management, warehouse operations, and finance. Miss a step, and you create: Inventory mismatch — on-hand quantities that don’t match physical stock Supplier master data drift — invoices tied to prices that no longer apply after returns Warehouse disruption — excess stock, blocked pallets, confusion on what’s usable Finance rework — accruals and liabilities out of sync This guide walks through how D365 F&O handles Return Purchase Orders, with focus on the warehouse return movement process that keeps physical and system inventory in sync. Procurement Warehouse Finance Original PO Return Order (linked to PO) Create Return order # referenced Transfer Journal Active Floor → Return Staging Ship Ship to Supplier RMA / credit note Credit Reconciled against Return OrderThe Return Purchase Order flow crosses three systems: a return order in Procurement drives a Transfer Journal in the Warehouse, which drives a credit reconciliation in Finance — connected end to end by the return order number. 2. The Core Flow: Return Purchase Order in D365 F&O Step 1: Create a Purchase Order Return When material needs to be returned to the supplier: Navigate: Accounts Payable → Purchase Orders → All Purchase Orders Select the original PO Action: Click Return Order (in the Ribbon, or Create menu) System creates a return order linked to the original PO, with: Supplier reference intact Quantities that can be adjusted (typically the original received quantity is pre-populated) Line item details copied from the original PO Status = Open Configuration: Return orders inherit the original PO’s delivery address and supplier terms, but warehouses can override the receiving location if the return flows through a different staging area. Accounts payable  ›  Purchase orders  ›  All purchase orders  ›  PO-000482  ›  Return order PostConfirmCancelPrint Return order numberRO-000091 StatusOpen Vendor accountV-10234 — Acme Components Inc. Original purchase orderPO-000482 WarehouseWH-MAIN Return-to locationRETURN_QA Item number Product name Qty to return Unit price Line amount IT-4021 Steel Bracket 40mm 25 $50.00 $1,250.00 IT-4088 Hex Bolt Set 10 $12.50 $125.00 Illustrative mockup of a Return Order form — not an actual product screenshot. The return order carries the original PO reference, vendor, and return-to location into the warehouse process. Step 2: Warehouse Return Movement Once the return order exists, the warehouse must physically move material out of active inventory. In D365, this happens via Transfer Journal or Movement Journal, depending on your configuration. 3. Warehouse Return Movement Options Active Floor Transfer Journal Return Staging QA Hold Returnable Not returnable Ship to Supplier Scrap / Write-offOnce material reaches Return Staging, the path splits: returnable stock ships back to the supplier under the return order; non-returnable stock is scrapped through an Inventory Adjustment journal. Option A: Transfer Journal (Used in Most Implementations) Used when material physically moves from one warehouse/location to another (e.g., floor → return staging area). Navigate: Inventory Management → Journals → Transfer Journal Create new journal entry Add lines: From warehouse: where the inventory currently sits To warehouse: return staging/quarantine area Item number, quantity, UOM Assign to Return Purchase Order (optional reference field) Post System adjusts on-hand in “From” warehouse, increases in “To” warehouse Inventory management  ›  Journal entries  ›  Transfer journal  ›  RETURN-TRANSFER-0091 PostValidateLinesInquiries Journal nameRETURN-TRANSFER StatusDraft ReferenceRO-000091 Posting date09/16/2026 Item number From warehouse / location To warehouse / location Quantity IT-4021 WH-MAIN / ACTIVE_FLOOR WH-MAIN / RETURN_QA 25 IT-4088 WH-MAIN / ACTIVE_FLOOR WH-MAIN / RETURN_QA 10 Illustrative mockup of a Transfer Journal — not an actual product screenshot. The Reference field carries the return order number so the warehouse movement stays traceable to Finance. Workflow:Active Floor → Return Staging → Ship to Supplier(Transfer Journal 1) → (Transfer Journal 2 or direct shipment) Option B: Inventory Adjustment Journal Used if you’re removing defective/unsaleable inventory entirely (scrap, damage). Navigate: Inventory Management → Journals → Inventory Adjustment Create new journal Add lines: Item, warehouse, quantity Mark as “Loss” or “Damage” Post On-hand reduced; writeoff recorded When to use: Only when the material cannot be returned to supplier (scrap/damage). Do NOT use for supplier returns—you lose the linkage to the Return Purchase Order. 4. The Critical Step: Linking Movements to Returns This is where many implementations stumble. Problem: You create a return order and move inventory, but the two are never connected. Result: Finance doesn’t know which returns have been physically staged Accounts payable credits the invoice without visibility into what’s actually coming back Warehouse can’t track which pallets belong to which return Without a Link With a Link Return Order Warehouse Movement Finance / AP Return Order Warehouse Movement Finance / AP Return Order # Return Order #Without a shared reference, the return order, the warehouse movement, and the finance record never connect. Carrying the return order number through the transfer journal and into the credit memo links all three. Solution: Before posting the movement journal, reference the Return Purchase Order number in a comment or custom field Or: Use a dedicated warehouse location code (e.g., RETURN_TO_[SUPPLIER_CODE]) that signals items are earmarked for return Approach: Set up … Continue reading How a Leading North American Commercial Vehicle Manufacturer Managed Return Purchase Orders and Warehouse Return Movements in Dynamics 365 Finance & Operations →

Watch first Your browser does not support embedded video. Download the video instead. Twenty seconds: what the agent does, and why per-user CRM security is the part that is actually hard. 01Summary A Houston-based cybersecurity firm wanted their sales team to ask plain-language questions about CRM data, “how many active leads do we have”, “summarise this account’s open opportunities”, without opening Dynamics 365 and building a view for every question. We built it as an Azure AI Foundry agent connected to Microsoft Dataverse over the Model Context Protocol (MCP). No custom data layer, no synchronised copy of CRM, no bespoke API surface. Dataverse itself is the MCP server, and Foundry’s Agent Service calls it as a tool. The agent runs on GPT-4.1 and took an afternoon to wire up. The two things that actually cost time were an OAuth redirect URL that Foundry generates after you click Connect, and an architecture decision we got wrong the first time: the tenant your agent lives in decides whether per-user CRM security is achievable at all. 02In This Blog A practitioner walkthrough of connecting Foundry to Dynamics 365 over MCP, written around the two failures that cost us the most time rather than the happy path alone. The Scenario: A sales team that wanted answers from CRM without building a view for every question. The Approach: Use Dataverse as a first-party MCP server rather than building an API layer and hand-written function schemas. The Action: Environment enablement, an Entra app registration, the MCP tool with OAuth identity passthrough, and the redirect URL nobody warns you about. The Outcome: Per-user security enforced by Dataverse itself, with no parallel permission model to maintain. The connection is an afternoon. The question of whose identity the agent is acting as decides whether any of it survives contact with production. Table of Contents 01Summary→ 02In This Blog→ 03Why MCP Changes the Shape of This Problem→ 04Enable the MCP Server and Allow Your Client→ 05Register the Entra App→ 06Add the MCP Tool to the Agent→ 07Close the Redirect URL Loop→ 08Consent, and the Consent Loop→ 09Calling the Agent From Your Own Application→ 10The Tenancy Trap→ 11Licensing→ 12Impact→ 13Conclusion→ 14FAQ→ 15Get in Touch→ 03Why MCP Changes the Shape of This Problem Before MCP, connecting an LLM to Dynamics 365 meant building glue: an API layer, a set of hand-written function definitions describing each operation, schema documentation maintained by hand, and a deployment to own forever. Every new table meant another function. The Model Context Protocol replaces that with a contract. The server advertises its tools, the model discovers them at runtime, and the plumbing is the same whichever client connects. Microsoft ships Dataverse as a first-party MCP server, so the glue layer is simply gone. Dataverse MCP endpointurl https://{organisation}.crm{n}.dynamics.com/api/mcp A preview endpoint exists at /api/mcp_preview with additional tools, gated behind a separate environment setting. Check your region number Environment URLs are not always .crm. Ours has been .crm4 on other engagements, and pointing at the wrong host produces an authentication failure that looks like a permissions problem. Confirm it in Power Apps under Settings, Session details. The tool surface Tool What it does search Searches table schemas and business skills by keyword search_data Searches structured and unstructured data describe Returns details for tables, records, schemas, skills and apps read_query Runs supported Dataverse SQL SELECT queries create_record Inserts a row, returns the Gcfb-mcp7fd update_record Updates an existing row delete_record Deletes a row, only after explicit user approval create_table, update_table, delete_table Schema operations upsert_skill, delete_skill Manages Dataverse skills and playbooks init_file_upload, commit_file_upload, file_download SAS-based file handling The tool names changed, and older tutorials are wrong describe_table, list_tables and fetch were removed and folded into describe. The tool previously called search, which searched data, is now search_data, and search now searches metadata. If you maintain an allow list or deny list by tool name in your client, this rename silently changes what your agent can do. Review it. 04Step 1: Enable the MCP Server and Allow Your Client This is admin work in the Power Platform admin center, not maker work, and it is where most failed attempts stall. Open the environment settingsPower Platform admin center, then Manage, then Environments. Open the target environment and select Settings on the command bar. Turn on the MCP serverExpand Product, select Features, find Dataverse Model Context Protocol, and enable Allow MCP clients to interact with Dataverse MCP server. Open the allowed client listSelect Advanced Settings. This is where non-Microsoft and custom clients are registered individually. Add your client and enable itCreate an Allowed MCP Client record carrying the Application Id of the app registration you create in Step 2, then set Is Enabled to Yes. Copilot Studio is enabled by default. Nothing else is. Your Foundry agent is not Copilot Studio, so it needs an explicit entry. This is the single most common cause of “the connection succeeded but the agent never calls the tool”. Field Value Name A readable label, for example Foundry Dataverse Agent Unique Name A unique identifier for the record Application Id The client (app) ID of the Entra app registration from Step 2 Is Enabled Yes Figure 1: The Allowed MCP Client record. Is Enabled defaults to No. Is Enabled defaults to No Saving the record is not the same as enabling it. A disabled record behaves identically to no record at all, and produces no error anywhere. Set it to Yes before you save. This is a chicken-and-egg with Step 2: you need the Application Id before you can fill this in. Create the app registration first, then come back. Two constraints worth knowing before you plan the rollout: Managing the MCP server through Advanced connector policies requires the environment to be a Managed Environment. The allow list applies only to the /api/mcp agent entrypoint. MCP-named custom APIs are ordinary Dataverse APIs and are not restricted by this setting. If you want the preview tools, enable Allow MCP clients to interact with Dataverse MCP server (Preview version) as a … Continue reading How We Connected an Azure AI Foundry Agent to Dynamics 365 Using the Dataverse MCP Server for a Texas Industrial Cybersecurity Company →