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Multiple Fabric Data Agent Tools on a Single Foundry Agent
a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } We are evaluating the Microsoft Fabric Data Agent integration with Azure AI Foundry Agents and are looking for clarification on the supported architecture. Scenario We would like to create a single Foundry Agent as the orchestrator and attach multiple Microsoft Fabric Data Agents as tools. Each Fabric Data Agent is registered as a separate Foundry tool representing a specific business domain. Executive Assistant Agent (Orchestrator) ├─ Too: Sales Fabric Data Agent ├─ Tool: Finance Fabric Data Agent └─ Tool: HR Fabric Data Agent The expectation is that the Foundry Agent would automatically select the appropriate Fabric Data Agent tool based on the user's request. Examples: "What was our Q2 revenue?" → Sales Fabric Data Agent tool "What is current headcount?" → HR Fabric Data Agent tool "Show budget variance by region." → Finance Fabric Data Agent tool Is it a supported scenario to attach multiple Microsoft Fabric Data Agent tools to a single Foundry Agent? We are unable to find documentation that explicitly states whether: Multiple Fabric Data Agent tools can be attached to the same Foundry Agent. Multiple Fabric tools are supported within a single agent configuration. The error Duplicate tool argument name: 'azure_fabric' indicates a configuration issue, SDK limitation, or unsupported architecture
APIM within Foundry
Dear Azure AI Foundry team at Microsoft, Please reconsider the current architecture and developer experience around AI observability and token analytics. As it stands today, customers are expected to assemble an entire distributed system — APIM, Azure Functions, Static Web Apps, App Insights, Log Analytics, custom SSE parsing, and additional infrastructure — just to answer very basic operational questions: Which users are consuming the most tokens? Which models are being used the most? What are our real-time streaming costs? Which subscriptions/projects are generating spend? Even worse, many of these solutions break down when using streamed/SSE AI responses because APIM policies are not designed to reliably process chunked AI streams and partial JSON bodies. So customers end up building increasingly complicated middleware pipelines for functionality that should already exist natively inside the platform. At the same time: Azure clearly has access to token and billing telemetry internally customers are still billed for usage yet customers themselves are not given equivalent real-time visibility or tooling That creates a frustrating disconnect, making it feel like a money grab when. It's like paying for groceries and not allowing customers to receive a receipt. Another major issue is API key management. Providing effectively a single project-level credential for enterprise AI workloads creates operational and governance limitations that make multi-user auditing unnecessarily difficult. Why in the world, would the foundry team design this with only api key per project? Is there a secret reason for this, other than annoying customers? To be blunt: the current system design feels massively overengineered for customers while simultaneously underdelivering on the core metrics enterprises actually need. AI platform teams should not need to build 10+ supporting Azure services just to approximate token analytics for a single Foundry project. Azure has excellent infrastructure capabilities overall, which is exactly why this experience is so surprising. But if the platform architecture and observability story for AI workloads do not improve soon, many organizations — including ours — will seriously evaluate moving to alternative cloud providers and AI gateway solutions that provide simpler and more transparent operational tooling. Please prioritize: native streaming token telemetry first-class SSE observability proper per-user/per-model analytics better API credential management simpler AI cost governance workflows Right now, the operational overhead compared to the value delivered is far too high.
Data Visualisation / Charting in Azure Foundry
Hi Foundry community, We are working on an agent that can query internal data sources, and are looking for ways that we can visualise data (think pie charts, bar charts, etc.). This would be consumed by end users through Copilot/Teams. However we are unable to find a way to do so, which is surprising given that you easily can create charts through M365 Copilot Chat and through Copilot Studio. We have tried using the 'Code Interpreter' tool, but the Teams/Copilot client UIs just do not render the results inline, either interactive or as an embedded image. They also do not give any option to download them. Has anyone tackled this before? How have you been able generate charts? Many thanks!Foundry Agent deployed to Copilot/Teams Can't Display Images Generated via Code Interpreter
Hello everyone, I’ve been developing an agent in the new Microsoft Foundry and enabled the Code Interpreter tool for it. In Agent Playground, I can successfully start a new chat and have the agent generate a chart/image using Code Interpreter. This works as expected in both the old and new Foundry experiences. However, after publishing the agent to Copilot/Teams for my organization, the same prompt that works in Agent Playground does not function properly. The agent appears to execute the code, but the image is not accessible in Teams. When reviewing the agent traces (via the Traces tab in Foundry), I can see that the agent generates a link to the image in the Code Interpreter sandbox environment, for example: `[Download the bar chart](sandbox:/mnt/data/bar_chart.png)` This works correctly within Foundry, but the sandbox path is not accessible from Teams, so the link fails there. Is there an officially supported way to surface Code Interpreter–generated files/images when the agent is deployed to Copilot/Teams, or is the recommended approach perhaps to implement a custom tool that uploads generated files to an external storage location (e.g., SharePoint, Blob Storage, or another file hosting service) and returns a publicly accessible link instead? I've been having trouble finding anything about this online. Any guidance would be greatly appreciated. Thank you!New AI Foundry not sending refresh tokens to MCP (401 after access token expiration)
Hello, When connecting an MCP server hosted as an Azure Function using OAuth Passthrough in New AI Foundry Playground, the connection is established successfully, the Microsoft login popup appears, authentication succeeds, and the first MCP request returns data correctly. However, once the access token expires, the Playground and deployed AI Foundry agents to Copilot/Teams do not appear to refresh the token, despite offline_access being included in the requested scopes and a refresh URL being configured. All subsequent MCP calls fail with 401 Unauthorized until the connection is manually recreated. For testing, we reduced the token lifetime to 10 minutes to make the issue easier to reproduce. Impact This prevents long-lived or repeated MCP usage in AI Foundry Playground because the connection becomes unusable after token expiry and requires manual reconnection. MCP server host: Azure Function MCP server configuration in New AI Foundry: Endpoint: https://mcp-test-obo-rls-fabric.azurewebsites.net/mcp Client ID: <client_id> (redacted) Auth URL: https://login.microsoftonline.com/tenant_id(redacted)/oauth2/v2.0/authorize Token URL: https://login.microsoftonline.com/tenant_id(redacted)/oauth2/v2.0/token Refresh URL: https://login.microsoftonline.com/tenant_id(redacted)/oauth2/v2.0/token Scopes: openid profile offline_access api://(App ID)/user_impersonation Redirect URI: https://global.consent.azure-apim.net/redirect/(redacted)-fabric-rls-mcp Error returned tool_user_error: Authentication failed when connecting to the MCP server: https://mcp-test-obo-rls-fabric.azurewebsites.net:443/mcp : Response status code does not indicate success: 401 (Unauthorized). Response body: {"code":401,"message":"IDX10223: Lifetime validation failed. The token is expired. ValidTo (UTC): '03/30/2026 08:19:28', Current time (UTC): '03/30/2026 08:29:55'."}. Verify your authentication headers. Suggestions: First verify the required permissions. If the access token is expired or revoked, recreate the connection. If this connection is shared by other users or workflows, recreate it carefully to avoid disruption. Function App / Identity Provider (Entra) The Azure Function authentication configuration: Identity provider: Microsoft (MCP-Fabric-RLS-Server) App registration: MCP-Fabric-RLS-Server Supported account types: Single tenant Application (client) ID: App ID Client secret setting name: MICROSOFT_PROVIDER_AUTHENTICATION_SECRET Issuer URL: https://login.microsoftonline.com/tenant_id(redacted)/v2.0 Redirect URI is added to App Authentication Redirect URI configuration as "Web". Allowed token audiences api://App ID App ID https://mcp-test-obo-rls-fabric.azurewebsites.net Additional checks enabled Allow requests from any application Allow requests from any identity Allow requests only from issuer tenant tenant_id (redacted) Notes The first authenticated call succeeds, so the initial OAuth flow appears to be working. The failure only occurs after the access token expires. Because offline_access is requested and the refresh URL is configured, our expectation is that the client should refresh the token automatically. Our working hypothesis is that either: the refresh token is not being issued, the refresh token is not being stored/used by AI Foundry Playground, or OAuth Passthrough for MCP connections in this scenario does not currently support automatic refresh as expected. Thank you for any assistance provided.Azure AI Foundry Agent Unable to Use Credentials Stored in Key Vault Through Playwright MCP Tool
Hello everyone, I am trying to understand how Azure AI Foundry agents interact with Azure Key Vault when using custom MCP tools, and I would appreciate any guidance from the community. My Setup - Created an Azure AI Foundry agent. - Created an Azure Key Vault and configured all permissions according to Microsoft's official documentation. - Stored the required website credentials (username and password) in the Key Vault. - Deployed the official Playwright MCP Docker image. - Exposed the MCP server using ngrok and verified that the endpoint is accessible. - Connected the MCP endpoint as a Custom MCP Tool in Azure AI Foundry. - Performed all configuration through the Azure portal, Foundry UI, and Playground only (no SDK or custom application code involved). The Issue The agent can access and use the Playwright MCP tool. However, when I ask it to log in to a website using credentials that are already stored in Key Vault, it does not populate the username and password fields. My expectation was that the agent would be able to retrieve the secrets from Key Vault and provide them to the Playwright tool during execution. Questions Is there currently a supported mechanism for Azure AI Foundry agents to automatically retrieve Key Vault secrets and pass them to a Custom MCP tool? Does the Playwright MCP Docker image have any built-in integration with Azure Key Vault? When using only the Foundry UI (without SDK code), can a Foundry agent securely inject Key Vault secrets into MCP tool calls? Are additional configurations required beyond Key Vault permissions and agent connections? Has anyone successfully implemented a similar setup where a Foundry agent uses credentials stored in Key Vault to perform browser automation through Playwright MCP? Any clarification on the expected architecture and whether this scenario is currently supported in Azure AI Foundry would be greatly appreciated. Thank you.
Unable to Connect Localhost MCP Server from Azure AI Foundry Hosted Agent (o4-mini)
I'm using the Azure AI Foundry Toolkit in VS Code and have configured an MCP server running on my local machine (localhost). When I run my Azure AI Foundry-hosted agent (o4-mini), it fails to connect to the MCP server. Based on the error logs, it appears that the hosted agent cannot reach the localhost endpoint. My understanding is that the MCP server is running correctly locally, but the hosted agent seems unable to access services running on my machine. Has anyone successfully connected a locally hosted MCP server to an Azure AI Foundry-hosted agent while using the Foundry Toolkit in VS Code?Failed to add tool to agent - Preview Feature Required?
Hi, We’ve recently run into an issue where we’re no longer able to add tools to our Foundry agent. This was previously working without problems in our development environment, but now every attempt results in the following error: “Failed to add tool to agent Request failed with status code 403.” After inspecting the request in the browser’s developer console, we noticed an additional message: "This operation requires the following opt-in preview feature(s): AgentEndpoints=V1Preview. Include the 'Foundry-Features: AgentEndpoints=V1Preview' header in your request." How can we opt in for this foundry preview feature? and when was this change introduced? We are unsure if the issue is related the the preview feature missing, or some other forbidden issue. Any help would be very much appreciated. Kind regards, Arne
