{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-teamsdevapp-vscode-ai-foundry","slug":"microsoft-foundry","name":"Microsoft Foundry","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=TeamsDevApp.vscode-ai-foundry","page_url":"https://unfragile.ai/microsoft-foundry","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_0","uri":"capability://tool.use.integration.azure.integrated.model.deployment.and.lifecycle.management","name":"azure-integrated model deployment and lifecycle management","description":"Enables deployment of pre-trained models (from Microsoft, OpenAI, Meta, DeepSeek catalogs) directly to Azure compute resources through a hierarchical resource explorer UI. The extension integrates with Azure subscription/resource group context to scope deployments, leveraging Azure RBAC for access control and managed identities for credential handling. Deployment workflow is triggered via command palette or sidebar navigation without requiring local model files or manual infrastructure provisioning.","intents":["Deploy a third-party LLM (e.g., GPT-4, Llama) to Azure without leaving VS Code","Switch between multiple deployed models across different Azure resource groups","Manage model lifecycle (deploy, test, iterate) within a single IDE window"],"best_for":["Azure-native development teams building AI applications","Enterprise developers requiring centralized model governance via Azure RBAC","Teams standardizing on Microsoft Foundry as their model deployment platform"],"limitations":["Only supports pre-deployed models; cannot train or fine-tune models within the extension","Requires Azure Container Registry (ACR) permissions for hosted agent deployment; extension cannot auto-assign roles without elevated Azure permissions","Model catalog is curated by Microsoft Foundry service; no support for arbitrary custom model sources","No offline deployment capability; requires live Azure subscription and network connectivity"],"requires":["Active Azure subscription with resource group","Microsoft Foundry project created in target Azure region","Azure Resources extension installed in VS Code","AI Toolkit for Visual Studio Code extension installed","Azure account authentication via VS Code Azure Account extension"],"input_types":["Model selection from catalog (UI-driven)","Azure subscription/resource group context"],"output_types":["Deployed model endpoint URL","Model metadata (name, version, provider)","Deployment status and logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_1","uri":"capability://text.generation.language.in.extension.model.playground.for.interactive.testing","name":"in-extension model playground for interactive testing","description":"Provides a built-in testing interface within VS Code to invoke deployed models with arbitrary prompts and inspect responses in real-time. The playground is scoped to the selected Microsoft Foundry project and communicates with deployed model endpoints via Azure-authenticated requests. Results are displayed inline without context switching to external tools or web consoles.","intents":["Test a deployed model's behavior with different prompts before integrating it into an application","Verify model output quality and latency without leaving the IDE","Iterate on prompt engineering and model selection within a single workflow"],"best_for":["Developers prototyping AI features and validating model behavior","Teams conducting rapid prompt iteration and A/B testing of model outputs","Solo developers building LLM-powered applications who want minimal context switching"],"limitations":["Limited to testing pre-deployed models only; cannot test models in training or fine-tuning states","No built-in prompt versioning or history management; each test is ephemeral","No batch testing or automated evaluation metrics; testing is manual and interactive only","Playground latency depends on Azure model endpoint performance; no local caching of responses"],"requires":["At least one deployed model in the selected Microsoft Foundry project","Network connectivity to Azure model endpoints","Azure authentication with read access to the model deployment"],"input_types":["Free-form text prompts","Model selection from deployed models list"],"output_types":["Model response text","Latency metrics","Token usage (if supported by model)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_2","uri":"capability://code.generation.editing.context.aware.sample.code.generation.from.deployed.models","name":"context-aware sample code generation from deployed models","description":"Generates boilerplate code snippets for consuming a selected deployed model via right-click context menu on models in the resource explorer. The generated code includes authentication setup (Azure SDK patterns), endpoint invocation, and response handling. Code generation is template-based and tailored to the selected model's API contract and the user's current project context.","intents":["Quickly scaffold code to call a deployed model from a new application","Copy-paste working examples of model invocation with proper Azure authentication","Reduce boilerplate setup time when integrating multiple models into an application"],"best_for":["Developers building proof-of-concept AI applications and wanting quick integration examples","Teams standardizing on Azure SDK patterns for model consumption","Developers unfamiliar with Azure authentication flows who benefit from generated examples"],"limitations":["Generated code is template-based boilerplate; does not adapt to project-specific patterns or existing codebase conventions","No support for generating code in languages other than those supported by Azure SDKs (likely C#, Python, JavaScript/TypeScript, Java)","Generated code does not include error handling, retry logic, or production-grade patterns; requires manual enhancement","No integration with workspace file system; generated code must be manually copied into project files"],"requires":["At least one deployed model in the selected Microsoft Foundry project","Azure SDK installed for the target language (Python, C#, JavaScript, etc.)","Azure authentication credentials available in the development environment"],"input_types":["Model selection via right-click context menu"],"output_types":["Code snippet (text)","Language: Python, C#, JavaScript, or other Azure SDK-supported languages"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_3","uri":"capability://planning.reasoning.agent.creation.deployment.and.testing.via.azure.ai.agent.service","name":"agent creation, deployment, and testing via azure ai agent service","description":"Enables creation of AI agents (autonomous or semi-autonomous systems that orchestrate model calls and tool invocations) within the extension, with deployment to Azure AI Agent Service and in-extension testing capabilities. The agent creation workflow is driven through command palette and sidebar UI, with agents stored as resources within the selected Microsoft Foundry project. Testing agents uses the same playground interface as model testing, allowing developers to invoke agents with prompts and inspect orchestration behavior.","intents":["Build and deploy a multi-step AI agent (e.g., retrieval-augmented generation, tool-calling agent) without leaving VS Code","Test agent behavior and tool invocations interactively before deploying to production","Manage agent lifecycle (create, test, deploy, iterate) within a single IDE window"],"best_for":["Teams building autonomous AI systems and requiring tight integration with Azure infrastructure","Developers prototyping agents and needing rapid iteration and testing cycles","Enterprise teams requiring centralized agent governance and deployment via Azure"],"limitations":["Agent deployment requires Azure AI Agent Service availability in the target region; not all Azure regions support this service","Hosted agent deployment is subject to Azure Container Registry (ACR) permission constraints; extension cannot auto-assign ACR roles without elevated Azure permissions, requiring manual workarounds","Agent orchestration logic must be defined via Azure AI Agent Service APIs or UI; extension does not provide a visual agent builder or low-code orchestration interface","No built-in agent versioning or rollback capabilities; agent updates overwrite previous versions","Agent testing is limited to interactive prompts; no batch testing, automated evaluation, or performance profiling within the extension"],"requires":["Azure AI Agent Service available in the target Azure region","Microsoft Foundry project with appropriate permissions","Azure Container Registry (ACR) with proper role assignments (Container Registry Repository Reader, Lister, Writer) for hosted agent deployment","Azure Resources extension and AI Toolkit extension installed","Azure account authentication with sufficient permissions to create and deploy agents"],"input_types":["Agent configuration (name, description, model selection, tools)","Test prompts for agent invocation"],"output_types":["Deployed agent endpoint URL","Agent metadata and configuration","Agent response and orchestration logs","Tool invocation traces"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_4","uri":"capability://search.retrieval.multi.provider.model.catalog.browsing.and.selection","name":"multi-provider model catalog browsing and selection","description":"Provides a curated, searchable catalog of pre-trained models from multiple providers (Microsoft, OpenAI, Meta, DeepSeek, and others) accessible via the sidebar resource explorer. The catalog is dynamically populated by the Microsoft Foundry service and allows developers to browse model metadata (name, provider, version, capabilities) and select models for deployment. Model selection is scoped to the current Azure subscription and resource group context.","intents":["Discover available models from multiple providers without visiting external marketplaces or documentation","Compare models from different providers (e.g., GPT-4 vs. Llama vs. DeepSeek) within a single interface","Select and deploy a model with a single click, without manual configuration or API key management"],"best_for":["Developers evaluating multiple models for a specific use case","Teams standardizing on a curated set of models via Microsoft Foundry","Organizations requiring centralized model governance and approval workflows"],"limitations":["Catalog is curated by Microsoft Foundry; custom or proprietary models cannot be added to the catalog","Model availability varies by Azure region; not all models are available in all regions","No built-in model comparison or evaluation tools; developers must manually compare model metadata","Catalog is read-only from the extension; model additions or updates are managed by Microsoft Foundry service"],"requires":["Network connectivity to Microsoft Foundry service","Azure subscription with access to the model catalog","Microsoft Foundry project created in the target Azure region"],"input_types":["Search query (optional)","Filter by provider (optional)"],"output_types":["Model list with metadata (name, provider, version, description)","Model selection for deployment"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_5","uri":"capability://tool.use.integration.project.scoped.resource.hierarchy.navigation.and.context.switching","name":"project-scoped resource hierarchy navigation and context switching","description":"Organizes deployed models, agents, and other resources in a hierarchical tree view (Azure Subscription → Resource Group → Microsoft Foundry Project → Resources) within the VS Code sidebar. Developers can expand/collapse nodes, search for resources, and switch between projects via the 'Select Default Project' command. The selected project context persists across VS Code sessions and is used to scope all subsequent operations (model deployment, agent creation, playground testing).","intents":["Navigate between multiple Microsoft Foundry projects without using the Azure Portal","View all deployed models and agents for a project in a single, organized view","Switch project context and have all extension operations automatically use the new project scope"],"best_for":["Teams managing multiple AI projects across different Azure subscriptions or resource groups","Developers working on multiple concurrent AI applications and needing quick context switching","Organizations with centralized Azure governance requiring resource hierarchy visibility"],"limitations":["Hierarchy is read-only from the extension; resource creation/deletion must be done via Azure Portal or Azure CLI","Search functionality is limited to resource names; no advanced filtering by resource type, creation date, or custom tags","Project switching requires explicit command invocation; no quick-switch UI or keyboard shortcut","Hierarchy refresh is manual or event-driven; no real-time sync if resources are modified outside the extension"],"requires":["At least one Microsoft Foundry project created in the Azure subscription","Azure Resources extension installed","Azure account authentication with read access to the subscription and resource groups"],"input_types":["Azure subscription selection","Resource group selection","Project selection via command palette"],"output_types":["Hierarchical resource tree","Selected project context (persisted)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_6","uri":"capability://safety.moderation.azure.rbac.enforced.access.control.and.credential.management","name":"azure rbac-enforced access control and credential management","description":"Delegates authentication and authorization to Azure's identity and access management (IAM) system via managed identities and role-based access control (RBAC). The extension uses VS Code's Azure Account extension to obtain Azure credentials and enforces RBAC policies at the resource level (subscription, resource group, project). Developers do not manage API keys or credentials directly; access is determined by their Azure role assignments (e.g., 'Contributor', 'Reader', 'Custom Role').","intents":["Ensure that only authorized developers can deploy models or create agents in a project","Centralize credential management via Azure IAM rather than managing API keys per developer","Audit and control access to AI resources using Azure's built-in RBAC and audit logging"],"best_for":["Enterprise teams requiring centralized identity and access management","Organizations with compliance requirements (SOC 2, HIPAA, etc.) that mandate RBAC","Teams using Azure as their primary cloud platform and wanting consistent IAM policies"],"limitations":["RBAC policies are defined at the Azure subscription/resource group level; no fine-grained, resource-level RBAC within the extension","Credential refresh and token management are handled by VS Code's Azure Account extension; extension has no direct control over token lifecycle","No support for service principals or managed identities for extension-based automation; only user-based authentication is supported","ACR permission errors during hosted agent deployment cannot be auto-resolved by the extension; manual role assignment is required as a workaround"],"requires":["Azure Account extension installed in VS Code","Azure subscription with appropriate RBAC roles assigned to the user","Azure AD tenant with the user's account","Network connectivity to Azure AD for token validation"],"input_types":["Azure credentials (via Azure Account extension)","RBAC role assignments (defined in Azure Portal)"],"output_types":["Authenticated access to Azure resources","Authorization decisions (allow/deny) based on RBAC policies"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_7","uri":"capability://tool.use.integration.workspace.agnostic.resource.management.without.file.system.integration","name":"workspace-agnostic resource management without file system integration","description":"Manages AI resources (models, agents, deployments) entirely through Azure cloud state, without requiring integration with the VS Code workspace file system or open editor context. All resource operations (deployment, testing, configuration) are stateless and scoped to the Azure subscription/resource group context. The extension does not read, modify, or depend on workspace files, allowing it to function independently of the developer's local project structure.","intents":["Manage AI resources in Azure without coupling them to a specific local project or codebase","Use the extension across multiple projects or workspaces without reconfiguring resource access","Maintain a clear separation between AI resource management (cloud) and application code (local)"],"best_for":["Teams with centralized AI infrastructure managed separately from application code","Developers working on multiple projects who want to reuse deployed models across projects","Organizations with dedicated MLOps teams managing AI resources independently of application developers"],"limitations":["Generated code snippets must be manually copied into project files; no automatic integration with workspace","No inline code suggestions or completions based on deployed models; all model integration is manual","No support for workspace-level configuration (e.g., .foundry.json or similar); all configuration is stored in Azure","Developers must manually manage the connection between local code and deployed Azure resources; no automatic linking or dependency tracking"],"requires":["Azure subscription and resource group context","No specific workspace file structure or configuration required"],"input_types":["Azure resource context (subscription, resource group, project)"],"output_types":["Cloud-based resource state (models, agents, deployments)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-teamsdevapp-vscode-ai-foundry__cap_8","uri":"capability://automation.workflow.command.palette.driven.workflow.automation","name":"command palette-driven workflow automation","description":"Exposes all extension operations through VS Code's command palette (Ctrl+Shift+P) using a consistent 'Microsoft Foundry: [Action]' naming convention. Commands include project selection, model deployment, agent creation, playground testing, and code generation. The command palette serves as the primary interaction method, allowing developers to invoke operations via keyboard shortcuts without navigating sidebar menus or context menus.","intents":["Quickly invoke extension operations using keyboard shortcuts without mouse navigation","Discover available operations by searching the command palette","Automate extension workflows via VS Code's command API (useful for custom keybindings or macros)"],"best_for":["Power users and developers who prefer keyboard-driven workflows","Teams building custom VS Code extensions that need to invoke Foundry operations programmatically","Developers who want to create custom keybindings for frequently-used operations"],"limitations":["Command palette search is text-based; no hierarchical or categorized command organization","No built-in command history or favorites; developers must re-search for commands each session","Custom keybindings require manual configuration in VS Code's keybindings.json; no UI for keybinding management","Command palette is modal and interrupts the current workflow; no non-blocking command invocation"],"requires":["VS Code with command palette enabled (default)","Microsoft Foundry extension installed"],"input_types":["Command name (text search)","Command arguments (if applicable)"],"output_types":["Command execution result","UI state changes (e.g., sidebar updates)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Active Azure subscription with resource group","Microsoft Foundry project created in target Azure region","Azure Resources extension installed in VS Code","AI Toolkit for Visual Studio Code extension installed","Azure account authentication via VS Code Azure Account extension","At least one deployed model in the selected Microsoft Foundry project","Network connectivity to Azure model endpoints","Azure authentication with read access to the model deployment","Azure SDK installed for the target language (Python, C#, JavaScript, etc.)","Azure authentication credentials available in the development environment"],"failure_modes":["Only supports pre-deployed models; cannot train or fine-tune models within the extension","Requires Azure Container Registry (ACR) permissions for hosted agent deployment; extension cannot auto-assign roles without elevated Azure permissions","Model catalog is curated by Microsoft Foundry service; no support for arbitrary custom model sources","No offline deployment capability; requires live Azure subscription and network connectivity","Limited to testing pre-deployed models only; cannot test models in training or fine-tuning states","No built-in prompt versioning or history management; each test is ephemeral","No batch testing or automated evaluation metrics; testing is manual and interactive only","Playground latency depends on Azure model endpoint performance; no local caching of responses","Generated code is template-based boilerplate; does not adapt to project-specific patterns or existing codebase conventions","No support for generating code in languages other than those supported by Azure SDKs (likely C#, Python, JavaScript/TypeScript, Java)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.79,"quality":0.28,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:29.937Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft-foundry","compare_url":"https://unfragile.ai/compare?artifact=microsoft-foundry"}},"signature":"Ov1DqSDhfRP54uMK8k0auhdlu2tSA22ef48SXZmUzCsshRvoKVephQHLfVo00e4jH2AUYKTrYfi4oRXjYOP+Dw==","signedAt":"2026-06-22T11:45:55.339Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft-foundry","artifact":"https://unfragile.ai/microsoft-foundry","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft-foundry","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}