Dataverse: Get Choice Labels & Values

Key insights

• Choice option sets in Microsoft Dataverse store user-facing labels and the integer values behind them.
  Use the Dataverse Web API to fetch these labels and values so apps and flows show the correct text.
• Call metadata endpoints such as OptionSetDefinitions or EntityDefinitions under /api/data/v9.2 to get option set definitions.
  The response lists each option with a label object and its numeric value.
• The JSON metadata returns fields like Name and Options, where each option includes Label (often inside LocalizedLabels) and Value.
  Parse these fields to build label–value pairs for UIs or logic.
• In Power Automate use the HTTP action or a Custom Connector to call the Web API, then parse the JSON in the flow.
  Authenticate with OAuth and grant the flow proper Dataverse permissions.
• Benefits include dynamic labels (no hardcoding), built-in localization, and easier automation for validation, display, or audits.
  Fetching metadata keeps apps resilient when option sets change.
• 2025 best practices: enable the Return Full Metadata option when available, use RetrieveMetadataChanges for efficient sync, and read formatted labels for user-friendly text.
  Cache results and refresh with metadata changes to improve performance.

Softchief Learn Releases Practical Guide on Dataverse Choice Metadata

Softchief Learn has published a focused tutorial that demonstrates how to retrieve Choice option sets metadata from Microsoft Dataverse using an HTTP action in Power Automate. The video walks viewers through calling the Dataverse Web API to fetch option items, including their display labels and integer values, and then parsing that metadata inside a flow. As a result, makers working on Power Apps, Power Automate, and Dynamics 365 projects can learn how to avoid hardcoding option labels and keep their automations aligned with system metadata. Overall, the tutorial aims to make label-value retrieval straightforward for everyday automation scenarios.

The presenter explains the common structure returned by the API, highlighting fields such as the choice set name and the options array that contains localized labels and values. Viewers are shown sample JSON responses that map labels to numeric values, helping illustrate why programs should read metadata rather than assume fixed labels. Consequently, the video is useful for teams that need runtime lookups for display logic, validation, or audit tasks. The pacing remains practical, with screen demos and real request examples to support adoption.

Step-by-Step: Using HTTP Actions to Query Metadata

Softchief Learn outlines the endpoints used to retrieve choice metadata, including global and local option set paths under the api/data/v9.x surface. The tutorial covers constructing an HTTP GET request, setting authentication headers, and handling the JSON payload that returns Label objects and their Value integers. Furthermore, the author demonstrates parsing strategies inside a Flow so that label-value pairs can be extracted into variables or used directly in expressions. This gives flow authors a clear pattern to follow when integrating metadata calls into longer automation sequences.

In addition, the video explains how a Custom Connector can streamline repeated metadata calls by encapsulating authentication and endpoints, thereby reducing repeated configuration inside multiple flows. The presenter shows how to account for localized labels by reading the LocalizedLabels array and selecting the appropriate language code. Thus, apps that must support multiple languages can dynamically present the correct text without manual mapping. These examples reinforce practical steps while keeping explanations accessible for intermediate makers.

Tradeoffs: Dynamic Retrieval Versus Caching

The video emphasizes the benefit of dynamic retrieval so apps remain resilient when option sets change, but it also discusses tradeoffs such as performance and API limits. Calling the metadata endpoints at runtime provides up-to-date labels, yet frequent requests may increase flow latency and consume API quotas, which could impact high-volume solutions. Therefore, Softchief Learn recommends balancing immediacy with efficiency by employing short-term caches or retrieving metadata during scheduled syncs. In this way, teams can reduce repetitive calls while still avoiding stale label mappings.

Another tradeoff covered is complexity versus maintainability: fetching metadata involves OAuth authentication and parsing nested JSON, which raises the initial development complexity. On the other hand, hardcoding values simplifies flows but increases maintenance burden when metadata changes or when supporting multiple environments. The presenter suggests using a hybrid approach: use cached metadata for normal operations and have on-demand metadata calls for administrative or uncommon paths. This compromise helps manage both performance and correctness over time.

Challenges in Implementation

Softchief Learn does not shy away from common implementation hurdles, such as configuring proper OAuth scopes, handling localized label selection, and dealing with global versus local option sets. Permissions must be carefully managed so flows have read access to metadata endpoints, and the video highlights that misconfigured scopes are a frequent cause of failed requests. Additionally, the JSON schema for metadata can be deep, so parsing logic must account for missing fields and multiple language labels to avoid runtime errors. These practical warnings help viewers anticipate and fix issues faster when they replicate the examples.

Another notable challenge is versioning: different Dataverse API versions return slightly different metadata shapes, and environments that run different versions may behave differently. To address this, the tutorial recommends testing against each target environment and taking advantage of the RetrieveMetadataChanges function for efficient synchronization when maintaining a local metadata cache. This proactive approach reduces surprises during deployment and ensures flows behave consistently across development, staging, and production environments.

Outlook and Best Practices for 2025

Looking ahead, Softchief Learn notes that recent platform updates have made metadata consumption easier, pointing to options like the Return Full Metadata parameter in Dataverse connectors and improved tooling for metadata queries. Consequently, makers can expect fewer manual parsing steps and smoother integrations when they adopt modern connector features. The author also encourages the use of custom connectors to encapsulate authentication and to centralize metadata logic, which promotes reuse and simplifies flow maintenance. These trends suggest that retrieving option set metadata will become quicker and more standardized over time.

In closing, the video provides a pragmatic guide for teams that need reliable access to Choice option sets in automation scenarios, while clearly explaining the tradeoffs and challenges they will face. By demonstrating concrete examples and recommending caching and synchronization patterns, Softchief Learn helps practitioners make informed decisions about when to call metadata endpoints and when to store results locally. Overall, the tutorial serves as a useful reference for anyone building Power Platform solutions that must display or validate option set values dynamically.

Keywords

Dataverse choice option set metadata, Get option set labels and values Dataverse, Power Automate HTTP action Dataverse metadata, Retrieve choice field metadata Dataverse, Dataverse option set label value API, HTTP request fetch option set metadata, Dataverse choice fields Power Automate HTTP, Read option set metadata using HTTP action