Enforce or Audit Policy Inheritance in API Management
We’re excited to announce a new Azure Policy definition that lets you enforce or audit policy inheritance in Azure API Management. With this capability, platform and governance teams can ensure that API Management policies are always inherited across all policy scopes — operations, APIs, products, and workspaces — strengthening consistency, compliance, and security across your API estate. Why this matters In Azure API Management, the <base /> policy element plays a critical role: it ensures that a runtime policy inherits policies defined at a higher scope, such as product, workspace, or all APIs (global). Without <base />, developers can inadvertently (or intentionally) bypass important platform rules, for example: Security controls like authentication or IP restrictions Operational requirements such as logging, tracing, or rate-limiting Business policies such as quota enforcement The result can be inconsistent behavior, compliance drift, and gaps in governance. How the new policy helps With the new Azure Policy definition, you can automatically ensure that <base /> is located at the start of each API Management policy section — <inbound>, <outbound>, <backend>, and <on-error> — across policies configured on operations, APIs, products, and workspaces. You can set the effect parameter to: Audit: Identify operation, API, product, or workspace policies where <base /> is missing. Deny: Prevent deployment of policies that do not include <base />. Get started To enable this new Azure Policy definition: Navigate to Azure Policy in the Azure portal. Select “Definitions” from the menu and choose “API Management policies should inherit parent scope policies using <base />”. In the policy definition view, select “Assign”. Configure the policy assignment scope, parameter (audit or deny), and other details. View built-in Azure Policy definitions for API Management.Update To API Management Workspaces Breaking Changes: Built-in Gateway & Tiers Support
What’s changing? If your API Management service uses preview workspaces on the built-in gateway and meets the tier-based limits below, those workspaces will continue to function as-is and will automatically transition to general availability once built-in gateway support is fully announced. API Management tier Limit of workspaces on built-in gateway Premium and Premium v2 Up to 30 workspaces Standard and Standard v2 Up to 5 workspaces Basic and Basic v2 Up to 1 workspace Developer Up to 1 workspace Why this change? We introduced the requirement for workspace gateways to improve reliability and scalability in large, federated API environments. While we continue to recommend workspace gateways, especially for scenarios that require greater scalability, isolation, and long-term flexibility, we understand that many customers have established workflows using the preview workspaces model or need workspaces support in non-Premium tiers. What’s not changing? Other aspects of the workspace-related breaking changes remain in effect. For example, service-level managed identities are not available within workspaces. In addition to workspaces support on the built-in gateway described in the section above, Premium and Premium v2 services will continue to support deploying workspaces with workspace gateways. Resources Workspaces in Azure API Management Original breaking changes announcements Reduced tier availability Requirement for workspace gatewaysBuilding Secure, Multi-User AI Workflows with the Responses API
With the recent GA (General Availability) of the Responses API, developers and enterprises now have access to a production-ready service purpose-built for stateful, multi-turn, tool-using AI agents. This milestone means you can confidently integrate the Responses API into real-world applications, knowing it’s fully supported, scalable, and designed for enterprise-grade use cases. Unlike traditional stateless APIs like Chat Completions, the Responses API maintains conversation history, supports tool orchestration, and enables multi-modal interactions. It’s ideal for building intelligent agents that need to remember context, call external tools, and interact with users over time. The Challenge: Securing AI Responses in Multi-User Environments As AI becomes more deeply embedded in enterprise apps, a new challenge emerges: response leakage. In multi-user environments, any user with a response ID could potentially access content they didn’t create—posing serious risks to privacy, data ownership, and compliance. By default, the Responses API allows retrieval of any response if you have the response ID. While this is convenient for prototyping, it’s not secure for production. There’s no built-in mechanism to verify who is making the request or whether they’re authorized to access that response. In this lab, I set out to solve that problem using Azure API Management (APIM). The goal? To ensure that only the user who created a response can retrieve or add to it, even if someone else has the response ID. This is especially important in scenarios where AI-generated content may include sensitive or proprietary information. The Problem: Response IDs Aren’t Enough The default behavior of the Responses API is simple: if you have a response ID, you can fetch the response. That’s convenient, but it’s also risky. There’s no built-in check to verify who is making the request. The Responses API is designed to be stateful, combining capabilities from chat completions and assistants into a unified experience. It’s powerful—but without additional safeguards, it can expose sensitive content to unintended users. This lab introduces a way to wrap the Responses API with APIM policies that enforce user-level access control. It’s a lightweight but powerful approach to securing AI-generated content. The Solution: APIM as a Gatekeeper Here’s how it works: A user sends a request to retrieve or update a response. APIM intercepts the request and extracts the user ID—either from the authentication token or, for testing purposes, from a custom header. APIM compares the user ID with the one associated with the response. If they match, the request proceeds. If not, it’s blocked. This ensures that only the original creator of a response can access or modify it. What’s in the Lab The lab in the AI Gateway repo includes: A sample API that mimics AI-generated responses. APIM policies that enforce user-level access. A test harness that lets you simulate requests with different user IDs. Header-based user ID injection for easier testing (ideal for labs and demos). This setup gives you a repeatable pattern for securing AI responses in production environments. Sample APIM Policy Snippet Here’s a simplified version of the APIM inbound policy that enforces user-level access: This policy checks the x-user-id header against the stored owner ID of the response. If they don’t match, the request is blocked with a 403 error. In a production scenario, you would want to use something other than just a userid in the header, for this I might suggest the userid from the authentication token. Why This Matters As AI becomes more embedded in our apps, we need to think beyond just securing the model—we need to secure the responses too. This lab shows how APIM can be used to: Enforce ownership of AI-generated content. Prevent unauthorized access to sensitive responses. Build trust into your AI workflows. Final Thoughts This lab is a great starting point for anyone building AI APIs in a multi-user environment. It’s simple, effective, and leverages tools you already know—like APIM. If you’re interested in extending this to token validation, role-based access, or integrating with Entra ID, let’s talk. I’d love to hear how you’re securing your AI stack.Announcing the availability of TLS 1.3 in Azure API Management in Preview
TLS 1.3 is the latest version of the internet’s most deployed security protocol, which encrypts data to provide a secure communication channel between two endpoints. TLS 1.3 support in Azure API Management is planned to rollout during the first week of February 2024. The rollout will happen in stages, this means some regions will get it first as we roll out globally.
Fetch Email of Login User In System Context
Dear Team, We are working on retrieving email address of the user joined to Entra ID from Entra-joined Windows devices, specifically while running in a system context.The whoami /upn command successfully returns the joined user’s email address in a user context, but it does not work in a system context, particularly when using an elevated terminal via the psexec utility.We also tested the dsregcmd /status command; however, in a system context, the User Identity tab in the SSO State section only appears when there is an error in AzureAdPrt. Under normal, healthy operating conditions, this command does not provide the user identity or the full domain username. We would greatly appreciate guidance on how to retrieve the Entra ID joined user’s email address in a system context, especially from those with prior experience in this area. Thank you for your support.
Expose REST APIs as MCP servers with Azure API Management and API Center (now in preview)
As AI-powered agents and large language models (LLMs) become central to modern application experiences, developers and enterprises need seamless, secure ways to connect these models to real-world data and capabilities. Today, we’re excited to introduce two powerful preview capabilities in the Azure API Management Platform: Expose REST APIs in Azure API Management as remote Model Context Protocol (MCP) servers Discover and manage MCP servers using API Center as a centralized enterprise registry Together, these updates help customers securely operationalize APIs for AI workloads and improve how APIs are managed and shared across organizations. Unlocking the value of AI through secure API integration While LLMs are incredibly capable, they are stateless and isolated unless connected to external tools and systems. Model Context Protocol (MCP) is an open standard designed to bridge this gap by allowing agents to invoke tools—such as APIs—via a standardized, JSON-RPC-based interface. With this release, Azure empowers you to operationalize your APIs for AI integration—securely, observably, and at scale. 1. Expose REST APIs as MCP servers with Azure API Management An MCP server exposes selected API operations to AI clients over JSON-RPC via HTTP or Server-Sent Events (SSE). These operations, referred to as “tools,” can be invoked by AI agents through natural language prompts. With this new capability, you can expose your existing REST APIs in Azure API Management as MCP servers—without rebuilding or rehosting them. Addressing common challenges Before this capability, customers faced several challenges when implementing MCP support: Duplicating development efforts: Building MCP servers from scratch often led to unnecessary work when existing REST APIs already provided much of the needed functionality. Security concerns: Server trust: Malicious servers could impersonate trusted ones. Credential management: Self-hosted MCP implementations often had to manage sensitive credentials like OAuth tokens. Registry and discovery: Without a centralized registry, discovering and managing MCP tools was manual and fragmented, making it hard to scale securely across teams. API Management now addresses these concerns by serving as a managed, policy-enforced hosting surface for MCP tools—offering centralized control, observability, and security. Benefits of using Azure API Management with MCP By exposing MCP servers through Azure API Management, customers gain: Centralized governance for API access, authentication, and usage policies Secure connectivity using OAuth 2.0 and subscription keys Granular control over which API operations are exposed to AI agents as tools Built-in observability through APIM’s monitoring and diagnostics features How it works MCP servers: In your API Management instance navigate to MCP servers Choose an API: + Create a new MCP Server and select the REST API you wish to expose. Configure the MCP Server: Select the API operations you want to expose as tools. These can be all or a subset of your API’s methods. Test and Integrate: Use tools like MCP Inspector or Visual Studio Code (in agent mode) to connect, test, and invoke the tools from your AI host. Getting started and availability This feature is now in public preview and being gradually rolled out to early access customers. To use the MCP server capability in Azure API Management: Prerequisites Your APIM instance must be on a SKUv1 tier: Premium, Standard, or Basic Your service must be enrolled in the AI Gateway early update group (activation may take up to 2 hours) Use the Azure Portal with feature flag: ➤ Append ?Microsoft_Azure_ApiManagement=mcp to your portal URL to access the MCP server configuration experience Note: Support for SKUv2 and broader availability will follow in upcoming updates. Full setup instructions and test guidance can be found via aka.ms/apimdocs/exportmcp. 2. Centralized MCP registry and discovery with Azure API Center As enterprises adopt MCP servers at scale, the need for a centralized, governed registry becomes critical. Azure API Center now provides this capability—serving as a single, enterprise-grade system of record for managing MCP endpoints. With API Center, teams can: Maintain a comprehensive inventory of MCP servers. Track version history, ownership, and metadata. Enforce governance policies across environments. Simplify compliance and reduce operational overhead. API Center also addresses enterprise-grade security by allowing administrators to define who can discover, access, and consume specific MCP servers—ensuring only authorized users can interact with sensitive tools. To support developer adoption, API Center includes: Semantic search and a modern discovery UI. Easy filtering based on capabilities, metadata, and usage context. Tight integration with Copilot Studio and GitHub Copilot, enabling developers to use MCP tools directly within their coding workflows. These capabilities reduce duplication, streamline workflows, and help teams securely scale MCP usage across the organization. Getting started This feature is now in preview and accessible to customers: https://aka.ms/apicenter/docs/mcp AI Gateway Lab | MCP Registry 3. What’s next These new previews are just the beginning. We're already working on: Azure API Management (APIM) Passthrough MCP server support We’re enabling APIM to act as a transparent proxy between your APIs and AI agents—no custom server logic needed. This will simplify onboarding and reduce operational overhead. Azure API Center (APIC) Deeper integration with Copilot Studio and VS Code Today, developers must perform manual steps to surface API Center data in Copilot workflows. We’re working to make this experience more visual and seamless, allowing developers to discover and consume MCP servers directly from familiar tools like VS Code and Copilot Studio. For questions or feedback, reach out to your Microsoft account team or visit: Azure API Management documentation Azure API Center documentation — The Azure API Management & API Center Teams🚀 New in Azure API Management: MCP in v2 SKUs + external MCP-compliant server support
Your APIs are becoming tools. Your users are becoming agents. Your platform needs to adapt. Azure API Management is becoming the secure, scalable control plane for connecting agents, tools, and APIs — with governance built in. -------------------------------------------------------------------------------------------------------------------------------------------------------------------- Today, we’re announcing two major updates to bring the power of the Model Context Protocol (MCP) in Azure API Management to more environments and scenarios: MCP support in v2 SKUs — now in public preview Expose existing MCP-compliant servers through API Management These features make it easier than ever to connect APIs and agents with enterprise-grade control—without rewriting your backends. Why MCP? MCP is an open protocol that enables AI agents—like GitHub Copilot, ChatGPT, and Azure OpenAI—to discover and invoke APIs as tools. It turns traditional REST APIs into structured, secure tools that agents can call during execution — powering real-time, context-aware workflows. Why API Management for MCP? Azure API Management is the single, secure control plane for exposing and governing MCP capabilities — whether from your REST APIs, Azure-hosted services, or external MCP-compliant runtimes. With built-in support for: Security using OAuth 2.1, Microsoft Entra ID, API keys, IP filtering, and rate limiting. Outbound token injection via Credential Manager with policy-based routing. Monitoring and diagnostics using Azure Monitor, Logs, and Application Insights. Discovery and reuse with Azure API Center integration. Comprehensive policy engine for request/response transformation, caching, validation, header manipulation, throttling, and more. …you get end-to-end governance for both inbound and outbound agent interactions — with no new infrastructure or code rewrites. ✅ What’s New? 1. MCP support in v2 SKUs Previously available only in classic tiers (Basic, Standard, Premium), MCP support is now in public preview for v2 SKUs — Basic v2, Standard v2, and Premium v2 — with no pre-requisites or manual enablement required. You can now: Expose any REST API as an MCP server in v2 SKUs Protect it with Microsoft Entra ID, keys or tokens Register tools in Azure API Center 2. Expose existing MCP-compliant servers (pass-through scenario) Already using tools hosted in Logic Apps, Azure Functions, LangChain or custom runtimes? Now you can govern those external tool servers by exposing them through API Management. Use API Management to: Secure external MCP servers with OAuth, rate limits, and Credential Manager Monitor and log usage with Azure Monitor and Application Insights Unify discovery with internal tools via Azure API Center 🔗 You bring the tools. API Management brings the governance. 🧭 What’s Next We’re actively expanding MCP capabilities in API Management: Tool-level access policies for granular governance Support for MCP resources and prompts to expand beyond tools 📚 Get Started 📘 Expose APIs as MCP servers 🌐 Connect external MCP servers 🔐 Secure access to MCP servers 🔎 Discover tools in API Center Summary Azure API Management is your single control plane for agents, tools and APIs — whether you're building internal copilots or connecting external toolchains. This preview unlocks more flexibility, less friction, and a secure foundation for the next wave of agent-powered applications. No new infrastructure. Secure by default. Built for the future.Logic Apps Aviators Newsletter - August 25
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month August Ace Aviator: Jenny Anderson What's your role and title? What are your responsibilities? I’m an Integration Architect at Tietoevry Tech Services, where I work with large enterprise customers to develop integration solutions. For the past two years my main focus has been on cloud and hybrid integrations. I design integration architectures, advise on best practices including security and the chosen architecture, and collaborate closely with development teams to implement and maintain these solutions. Can you give us some insights into your day-to-day activities and what a typical day in your role looks like? My days usually start with scrum meetings across ongoing projects, which help me stay updated on progress, align with teams and prioritize my tasks for the day. After that, I often have customer meetings where I advise on integration strategies, provide architectural guidance or work on pre-sales engagements to scope out potential solutions. Recently, a big focus has been on BizTalk migrations, helping customers modernize their integration platforms by moving to Azure-based solutions. I try to dedicate my afternoons to hands-on technical work, which I really enjoy. Lately, that’s involved working with the new hybrid capabilities in Logic Apps. It’s a great mix of strategic consulting and deep technical implementation, which keeps the role dynamic and rewarding. What motivates and inspires you to be an active member of the Aviators/Microsoft community? I’ve always received a lot of support from the community especially when I was starting out in my career and I still benefit from it today. That generosity and openness made a big impact on me, so I feel it’s important to give back whenever I can. For me it’s a way to pay it forward and stay connected to a network that has helped me grow both technically and professionally. Looking back, what advice do you wish you had been given earlier that you'd now share with those looking to get into STEM/technology? Don’t overthink it, just start doing! In the beginning of my career, I assumed that everyone else knew everything, and that I couldn’t contribute or be part of certain areas because I didn’t know enough. But the truth is, no one knows everything, and that’s completely okay. The best way to learn is by doing and taking on challenges, making mistakes and growing from experience. I believe confidence comes from action, not from waiting until you feel “ready.” What has helped you grow professionally? One thing that has really helped me grow is surrounding myself with people who have different experiences or areas of expertise, whether at work, in communities, or through networking. I’ve learned a lot simply by asking questions, even the ones that might seem obvious. I also try to say yes to new opportunities, especially when they push me outside my comfort zone. Being an overthinker, I’ve developed a personal mantra: “Think 40%, do 60%.” It reminds me not to get stuck in planning or doubt, but to take action and learn along the way. That mindset has really helped me move forward. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? If I could use a bit of magic in Logic Apps, I’d want AI to automagically handle all the data mappings. It’s honestly my least favorite part of integration work. It takes forever, it’s a bit dull and yet it’s always important. So, if AI could just step in and quietly take care of it, I wouldn’t complain. I’ve also heard a few customers ask for a disconnected control plane that can be hosted on-premises. That would be a big win for scenarios where cloud access is limited or compliance rules are extra strict. News from our product group Logic Apps Live July 2025 Missed Logic Apps Live in July? You can watch it here. We had a sneak peek into Logic Apps MCP Servers and Python support for Agent loop. Excinting topics and worth a watch! Troubleshoot Az Module within Logic App Standard Learn how to resolve Az Module installation failures in Logic Apps due to network restrictions or storage limits. Quick tests and fixes included to keep your workflows running smoothly. Introducing API Management Support in the Azure SRE Agent Azure’s SRE Agent now supports API Management, offering real-time diagnostics, backend health visualization, and intelligent remediation to keep your APIs reliable and scalable. Launch Your Private MCP Registry with Azure API Center. Discover how to create a secure, governed, and enterprise-ready MCP registry using Azure API Center—empowering AI innovation while maintaining control and visibility. Perform video analysis by using Azure Machine Learning and Computer Vision Replace manual video review with a scalable, AI-powered pipeline using Azure Machine Learning, Logic Apps, and Computer Vision. Boost accuracy and efficiency across industries like agriculture, traffic control, and manufacturing. Bringing Azure Logic Apps to on-prem, private, or public cloud with new Hybrid model | Azure Friday In this video Scott Hanselman and Harold Campos discuss the new Logic Apps Hybrid deployment model that allows customers to run their integration workloads in their own Kubernetes environments. This is ideal for customers initiating their journey to the cloud and hosting multiple on-premises workloads, who need to meet industry regulations, who wants to reuse their own Kubernetes infrastructure, or to avoid the natural latency introduced in hybrid configurations. News from our community Exposing Logic Apps as MCP Server in Azure API Management Video by Kent Weare On top of his PM work, Kent also finds time to keep his personal YouTube channel quite active. This time, he shows a walkthrough of creating an MCP Server using Logic Apps and API Management. The initial explanation of MCP and the various protocols alone make this video a great watch! Integration Love Story - Divya Swarnkar Video by Ahmed Bayoumy and Robin Wilde In this short episode of Integration Love Story, Ahmed and Robin chat with our own Divya Swarnkar, Product Manager at Microsoft who's been on an incredible journey from using Logic Apps as a customer to now helping build the product with the team behind the scenes. From BizTalk to Azure: A Guide for the Slightly Terrified Post by Sandro Pereira Explore the risks, timelines, and migration strategies as BizTalk nears end-of-life. Sandro shares the webinar recording – another tool to help you decide whether to stay or move to Azure Integration Services—without losing sleep. Azure Logic Apps Naming Conventions whitepaper Post by Sandro Pereira Boost clarity, scalability, and collaboration in Azure Logic Apps with this whitepaper. Learn best practices for naming triggers, actions, variables, and more - essential for automation, CI/CD, and long-term maintainability. You can create and use your own personal templates in Azure Logic Apps Post by Sandro Pereira It is not a newsletter, without at least a Friday Fact from Sandro! In this post, you can learn how to build, manage, and share reusable templates for consistent, efficient integration across projects. Speed up automation and standardize workflows you’re your own personal templates in Logic Apps.Announcing Public Preview of API Management WordPress plugin to build customized developer portals
Azure API Management WordPress plugin enables our customers to leverage the power of WordPress to build their own unique developer portal. API managers and administrators can bring up a new developer portal in a matter of few minutes and customize the theme, layout, add stylesheet or localize the portal into different languages.Build. Secure. Launch Your Private MCP Registry with Azure API Center.
We are thrilled to embrace a new era in the world of MCP registries. As organizations increasingly build and consume MCP servers, the need for a secure, governed, robust and easily discoverable tools catalog has become critical. Today, we are excited to show you how to do just that with MCP Center, a live example demonstrating how Azure API Center (APIC) can serve as a private and enterprise-ready MCP registry. The registry puts your MCPs just one click away for developers, ensuring no setup fuss and a direct path to coding brilliance. Why a private registry? 🤔 Public OSS registries have been instrumental in driving growth and innovation across the MCP ecosystem. But as adoption scales, so does the need for tighter security, governance, and control, this is where private MCP registries step in. This is where Azure API Center steps in. Azure API Center offers a powerful and centralized approach to MCP discovery and governance across diverse teams and services within an organization. Let's delve into the key benefits of leveraging a private MCP registry with Azure API Center. Security and Trust: The Foundation of AI Adoption Review and Verification: Public registries, by their open nature, accept submissions from a wide range of developers. This can introduce risks from tools with limited security practices or even malicious intent. A private registry empowers your organization to thoroughly review and verify every MCP server before it becomes accessible to internal developers or AI agents (like Copilot Studio and AI Foundry). This eliminates the risk of introducing random, potentially vulnerable first or third-party tools into your ecosystem. Reduced Attack Surface: By controlling which MCP servers are accessible, organizations significantly shrink their potential attack surface. When your AI agents interact solely with known and secure internal tools, the likelihood of external attackers exploiting vulnerabilities in unvetted solutions is drastically reduced. Enterprise-Grade Authentication and Authorization: Private registries enable the enforcement of your existing robust enterprise authentication and authorization mechanisms (e.g., OAuth 2) across all MCP servers. Public registries, in contrast, may have varying or less stringent authentication requirements. Enforced AI Gateway Control (Azure API Management): Beyond vetting, a private registry enables organizations to route all MCP server traffic through an AI gateway such as Azure API Management. This ensures that every interaction, whether internal or external, adheres to strict security policies, including centralized authentication, authorization, rate limiting, and threat protection, creating a secure front for your AI services. Governance and Control: Navigating the AI Landscape with Confidence Centralized Oversight and "Single Source of Truth": A private registry provides a centralized "single source of truth" for all AI-related tools and data connections within your organization. This empowers comprehensive oversight of AI initiatives, clearly identifying ownership and accountability for each MCP server. Preventing "Shadow AI": Without a formal registry, individual teams might independently develop or integrate AI tools, leading to "shadow AI" – unmanaged and unmonitored AI deployments that can pose significant risks. A private registry encourages a standardized approach, bringing all AI tools under central governance and visibility. Tailored Tool Development: Organizations can develop and host MCP servers specifically tailored to their unique needs and requirements. This means optimized efficiency and utility, providing specialized tools you won't typically find in broader public registries. Simplified Integration and Accelerated Development: A well-managed private registry simplifies the discovery and integration of internal tools for your AI developers. This significantly accelerates the development and deployment of AI-powered applications, fostering innovation. Good news! Azure API Center can be created for free in any Azure subscription. You can find a detailed guide to help you get started: Inventory and Discover MCP Servers in Your API Center - Azure API Center Get involved 💡 Your remote MCP server can be discoverable on API Center’s MCP Discovery page today! Bring your MCP server and reach Azure customers! These Microsoft partners are shaping the future of the MCP ecosystem by making their remote MCP Servers discoverable via API Center’s MCP Discovery page. Early Partners: Atlassian – Connect to Jira and Confluence for issue tracking and documentation Box – Use Box to securely store, manage and share your photos, videos, and documents in the cloud Neon – Manage and query Neon Postgres databases with natural language Pipedream – Add 1000s of APIs with built-in authentication and 10,000+ tools to your AI assistant or agent - coming soon - Stripe – Payment processing and financial infrastructure tools If partners would like their remote MCP servers to be featured in our Discover Panel, reach out to us here: GitHub/mcp-center and comment under the following GitHub issue: MCP Server Onboarding Request Ready to Get Started? 🚀 Modernize your AI strategy and empower your teams with enhanced discovery, security, and governance of agentic tools. Now's the time to explore creating your own private enterprise MCP registry. Check out MCP Center, a public showcase demonstrating how you can build your own enterprise MCP registry - MCP Center - Build Your Own Enterprise MCP Registry - or go ahead and create your Azure API Center today!
