Azure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. New Cool Kid in Town - MCP AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients MCP Protocol: Transport layer in the middle At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. You can create your own custom MCP Servers that connect to your or organizational data sources. For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote Remote vs. Local MCP Servers The MCP standard supports two modes of operation: Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Azure API Management as the AI Auth Gateway Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). MCP - Current Authorization Challenges State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: The MCP server is now treated as both a resource server AND an authorization server. This dual role has fundamental implications for MCP server developers and runtime operations. 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth The Authorization Flow The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram Conclusion Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. API Management: An Enterprise Solution for Securing MCP Servers Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! Acknowledgments This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. Additional Resources MCP Client Server integration with APIM as AI gateway Blog Post: https://aka.ms/remote-mcp-apim-auth-blog Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth Python: https://aka.ms/mcp-remote-apim-auth .NET: https://aka.ms/mcp-remote-apim-auth-dotnet On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample 3rd Party APIs – Backend Auth via Credential Manager: Blog Post: https://aka.ms/remote-mcp-apim-lab-blog APIM lab: https://aka.ms/ai-gateway-lab-mcp YouTube Video: https://aka.ms/ai-gateway-lab-demo
Designing and running a Generative AI Platform based on Azure AI Gateway
Are you in a platform team who has been tasked with building an AI Platform to serve the needs of your internal consumers? What does that mean? It’s a daunting challenge to be set, and even harder if you’re operating in a highly regulated environment. As enterprises scale out usage of Generative AI past a few initial use-cases they will face into a new set of challenges - scaling, onboarding, security and compliance to name a few. In this article we outline a set of common requirements and provide a reference implementation for an AI Platform.📢Announcing agent loop: Build AI Agents in Azure Logic Apps 🤖
This post is written in collaboration with Kent Weare and Rohitha Hewawasam The era of intelligent business processes has arrived! Today, we are excited to announce agent loop, a groundbreaking new capability in Azure Logic Apps to build AI agents into your enterprise workflows. With agent loop, you can embed advanced AI decision-making directly into your processes – enabling your apps and automation to not just follow predefined steps, but to reason, adapt, and act autonomously towards goals. Agent loop becomes central to AI Agent development — it’s a new action type that brings together your AI model of choice, domain-specific tools, and enterprise knowledge sources. Whether you’re building an autonomous agent to process loan approvals, a conversational agent to support customers, or a multi-agent system that coordinates tasks such as Sales Report generation across agents, Agent Loop enables your workflows to go beyond static steps — making decisions, adapting to context, and delivering outcomes. Agent loop is implemented using kernel object in the Semantic Kernel. The kernel object, along with an LLM, creates the plan for what needs to be done, while Logic Apps runtime handles execution of that plan. Agent Loop is highly configurable, enabling you to build agents with diverse capabilities: Conversational or Autonomous Agents With Logic Apps' extensive gallery of connectors, you can build fully autonomous agents that respond to real-time events — like new records in a database, files added to a share, or messages in a queue. Agent Loop also supports conversational agents via Channels, allowing agents to interact with users through the Azure portal or custom chat clients. Bring your own Model Associate your AI agent with any Azure OpenAI model of your choice. As new models become available, you can easily switch or upgrade without re-architecting the solution. Define Agent Goals and Guardrails Specify your agent’s objective and behavioral boundaries through system prompts and user instructions. Using connectors like Outlook or Teams, you can easily introduce human-in-the-loop interactions for approvals or overrides — enabling safe, controlled autonomy. Tools and Knowledge, Built In Leverage hundreds of out-of-the-box connectors to equip agents with access to enterprise systems, APIs, and business data. Enrich their reasoning with knowledge from vector stores, structured databases, or unstructured files, and empower them to take meaningful actions across your environment. AI Agents in Action Here are some examples of AI Agents in Action that highlight the value and efficiencies of these agents across different domains and solution areas. A product return agent verifies order details, return eligibility, and refund rules, then processes the return or requests additional info from the customer. A loan approval agent evaluates credit score, income, and risk profile, applies business rules, and auto-approves or routes applications for review. A recruiting agent screens resumes, summarizes qualifications, and drafts personalized outreach to top candidates, streamlining early hiring stages. A sales report generation workflow uses a writer agent to draft content, a reviewer agent to verify accuracy, and a publisher agent to format and distribute the report. An IT operations agent triages alerts, checks recent changes, and either resolves common issues or escalates to on-call engineers when needed. A multi-agent retail supply chain solution combines inventory and logistics agents to ensure timely restocks and optimize fulfillment routes. Why agent loop matters Modern businesses thrive on agility and intelligence. Traditional workflows remain essential for deterministic tasks—especially those involving structured data or high-risk decisions. But when processes involve unstructured data, changing context, or require adaptive decision-making, AI agents excel. They can reason, act in real time, and dynamically sequence steps to meet goals. Agent Loop exactly serves this purpose. What makes Agent Loop especially powerful is its deep integration with the Logic Apps ecosystem. Logic Apps comes with over 1,400+ connectors for Microsoft and third-party services – from databases and ERP systems to SaaS applications and custom APIs. They can also invoke custom code and scripts, making it easy to tap into homegrown capabilities. The agent isn’t limited to information in its prompt; it can actively retrieve knowledge, perform transactions, and effect change in the real world via these connectors. Logic Apps is uniquely positioned to enable customers to leverage their API and connector ecosystem cohesively across their workflows and AI Agents to build agentic applications. Equally important, Agent Loop is designed for flexibility. You can orchestrate single-agent workflows or coordinate multiple agents working in tandem towards a common goal. Agent Loop can even involve humans in the loop when needed – for instance, pausing to get a manager’s approval or to ask for clarification – leveraging Logic Apps’ human workflow capabilities. All of this is handled within the familiar, visual Logic Apps designer, so you get a high-level view of the entire orchestration. How agent loop works At a high level, Agent Loop works by pairing the reasoning capabilities of large-scale AI models with the robust action framework of Logic Apps. Built on top of Semantic Kernel, the Agent loop operates in iterative cycles, allowing the agent to think, act, and learn from each step: Reasoning (Think): The agent (powered by an LLM like Azure OpenAI Service under the hood) and on Semantic Kernel, examines its goal and the current context. It decides what needs to be done next – whether that’s gathering more information, calling a specific connector, or formulating an answer. This step is essentially the AI “planning” its next action based on the goal you’ve provided and the data it has so far. Action (Act): The agent then carries out the decided action by invoking a tool or connector through Logic Apps. This could be anything from querying a database, calling a REST API, sending an email, to running a calculation. Thanks to Logic Apps’ extensive connector library, the agent has a rich toolbox at its disposal. Each action is executed as a Logic Apps step, meaning it’s secure, managed, and logged like any other workflow action. Reflection (Learn): After the action, the agent receives the results (e.g. data retrieved, outcome of the API call, user input, etc.). It then evaluates: Did this bring it closer to the goal? Does the plan need adjusting? The agent updates its understanding based on new information. This reflection is what lets the agent handle complex, open-ended tasks – it can correct course if needed, try alternative approaches, or conclude if the goal has been satisfied. These steps repeat in a loop. The Agent Loop action manages this cycle automatically – calling the AI model to reason, executing the chosen connector operations, feeding results back, and iterating. Why Build AI Agents in Logic Apps? Building AI agents is an emerging frontier in automation but doing it from the ground up can be daunting especially when organizations build them in large numbers. Agent Loop in Logic Apps makes this dramatically easier and more scalable for several reasons: Declarative Orchestration: Logic Apps provides a visual workflow canvas and a serverless runtime. The Agent Loop action plugs into this and the platform handles the sequence of steps and iterations, so you can focus on defining the goal and selecting the connectors (tools) the agent can use. Code extensibility: Logic Apps supports both declarative and code-first approaches to building agents. You can combine the two — using visual designer for orchestration and injecting code where needed through extensibility points. Write custom logic in C#, PowerShell, JavaScript, or use inline scripts for lightweight processing. Python support is coming soon, enabling even more flexibility. 1400+ Integrated Tools: With the rich connector ecosystem at its disposal, your agent can seamlessly tap into your enterprise systems and SaaS applications. Your entire ecosystem of connectors, APIs, custom code and agents can be used by deterministic workflows and agents to solve business problems Observability: Logic Apps offers full traceability into each agent’s decisions and actions. Every run is logged in the workflow history, with data stored within the customer’s own network and storage boundaries. The Agent Chat view provides insights into the agent’s reasoning, tool invocations, and goal progress. Developers can easily revisit these logs for debugging, auditing, or analysis. Enterprise-Grade Governance: Because it runs on Azure Logic Apps, agent loop inherits all the robust monitoring, logging, security and compliance capabilities of the platform You can secure connections with managed identities and leverage built-in rate limiting, retries, and exception handling. Your AI agents run with the same enterprise-ready guardrails as any mission-critical workflow. Human-in-the-Loop & Multi-Agent Coordination: Logic Apps makes it straightforward to involve people at key decision points or to coordinate multiple agents. You can chain Agent Loop actions or have agents invoke other workflows, enabling collaborative problem-solving that would be difficult to implement from scratch. The result is a system where AI and humans can smoothly interact and complement each other. Faster Time to Value: By eliminating the boilerplate work of building an agent architecture (managing memory, planning logic, connecting to services, etc.), Agent Loop lets developers and architects concentrate on high-value logic and business goals, accelerating how you bring AI-driven improvements to your business processes. In short, agent loop combines the brains of generative AI with the brawn of Azure’s integration platform. It offers a turnkey way to build sophisticated AI-driven automation without reinventing the wheel. Companies no longer have to choose between the flexibility of custom AI solutions and the convenience of a managed workflow service – with Logic Apps and Agent Loop, you get both. Getting Started Agent Loop is available in Logic Apps Standard starting today! Here are some resources to help you begin: Documentation: Explore the agent loop concepts and detailed guide with step-by-step instructions on how to configure and use Agent Loop. Samples & Demos: Watch pre-recorded demos showcasing both conversational and autonomous agent scenarios built with Agent Loop. You'll also get a preview of exciting features coming soon. Looking Ahead Agent Loop opens up a new realm of possibilities for what you can achieve with Azure Logic Apps. It blurs the line between application integration and AI, allowing workflows to evolve from static sequences into adaptive, self-directed processes. We can’t wait to see what you will build with Agent Loop! This is just the beginning. We’re actively investing in new capabilities that are planned for release soon Multi-agent Hand-off Support – A multi-agent application with hand-off capabilities enables different agent-loops to collaborate by transferring tasks between one another based on expertise or context, which is crucial for building agentic applications that can dynamically adapt to complex, evolving goals and user needs. A2A (Agent-to-Agent) protocol support – A2A is a communication standard that defines how autonomous agents exchange messages, share context, and coordinate actions in a secure and structured way. It’s especially important in building agentic applications because it ensures interoperability, enables seamless hand-offs between agents, and maintains context integrity across different agents working toward a shared goal. This will allow Logic Apps agents to seamlessly integrate with other agentic platforms. OBO Auth for Logic Apps Agents: On Behalf Of Auth support for logic Apps agents would allow Logic Apps agents to use logged-in users identity for authentication when invoking Logic Apps connectors as part of agent-loop execution. This will enable building conversational applications to dynamically perform OAuth flows for fetching consent from log-in users to invoke Logic Apps connectors on logged-in user’s behalf. Contact Us Have feedback or questions about Agent Loop? We’d love to hear from you. Reply directly to this blog post or reach out to us through this form. Your input helps shape the future of Logic Apps and agentic automation.
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 TeamsBuild. 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!Expanding GenAI Gateway Capabilities in Azure API Management
In May 2024, we introduced GenAI Gateway capabilities – a set of features designed specifically for GenAI use cases. Today, we are happy to announce that we are adding new policies to support a wider range of large language models through Azure AI Model Inference API. These new policies work in a similar way to the previously announced capabilities, but now can be used with a wider range of LLMs. Azure AI Model Inference API enables you to consume the capabilities of models, available in Azure AI model catalog, in a uniform and consistent way. It allows you to talk with different models in Azure AI Studio without changing the underlying code. Working with large language models presents unique challenges, particularly around managing token resources. Token consumption impacts cost and performance of intelligent apps calling the same model, making it crucial to have robust mechanisms for monitoring and controlling token usage. The new policies aim to address challenges by providing detailed insights and control over token resources, ensuring efficient and cost-effective use of models deployed in Azure AI Studio. LLM Token Limit Policy LLM Token Limit policy (preview) provides the flexibility to define and enforce token limits when interacting with large language models available through the Azure AI Model Inference API. Key Features Configurable Token Limits: Set token limits for requests to control costs and manage resource usage effectively Prevents Overuse: Automatically blocks requests that exceed the token limit, ensuring fair use and eliminating the noisy neighbour problem Seamless Integration: Works seamlessly with existing applications, requiring no changes to your application configuration Learn more about this policy here. LLM Emit Token Metric Policy LLM Emit Token Metric policy (preview) provides detailed metrics on token usage, enabling better cost management and insights into model usage across your application portfolio. Key Features Real-Time Monitoring: Emit metrics in real-time to monitor token consumption. Detailed Insights: Gain insights into token usage patterns to identify and mitigate high-usage scenarios Cost Management: Split token usage by any custom dimension to attribute cost to different teams, departments, or applications Learn more about this policy here. LLM Semantic Caching Policy LLM Semantic Caching policy (preview) is designed to reduce latency and reduce token consumption by caching responses based on the semantic content of prompts. Key Features Reduced Latency: Cache responses to frequently requested queries based to decrease response times. Improved Efficiency: Optimize resource utilization by reducing redundant model inferences. Content-Based Caching: Leverages semantic similarity to determine which response to retrieve from cache Learn more about this policy here. Get Started with Azure AI Model Inference API and Azure API Management We are committed to continuously improving our platform and providing the tools you need to leverage the full potential of large language models. Stay tuned as we roll out these new policies across all regions and watch for further updates and enhancements as we continue to expand our capabilities. Get started today and bring your intelligent application development to the next level with Azure API Management.Enhancing AI Integrations with MCP and Azure API Management
As AI Agents and assistants become increasingly central to modern applications and experiences, the need for seamless, secure integration with external tools and data sources is more critical than ever. The Model Context Protocol (MCP) is emerging as a key open standard enabling these integrations - allowing AI models to interact with APIs, Databases and other services in a consistent, scalable way. Understanding MCP MCP utilizes a client-host-server architecture built upon JSON-RPC 2.0 for messaging. Communication between clients and servers occurs over defined transport layers, primarily: stdio: Standard input/output, suitable for efficient communication when the client and server run on the same machine. HTTP with Server-Sent Events (SSE): Uses HTTP POST for client-to-server messages and SSE for server-to-client messages, enabling communication over networks, including remote servers. Why MCP Matters While Large Language Models (LLMs) are powerful, their utility is often limited by their inability to access real-time or proprietary data. Traditionally, integrating new data sources or tools required custom connectors/ implementations and significant engineering efforts. MCP addresses this by providing a unified protocol for connecting agents to both local and remote data sources - unifying and streamlining integrations. Leveraging Azure API Management for remote MCP servers Azure API Management is a fully managed platform for publishing, securing, and monitoring APIs. By treating MCP server endpoints as other backend APIs, organizations can apply familiar governance, security, and operational controls. With MCP adoption, the need for robust management of these backend services will intensify. API Management retains a vital role in governing these underlying assets by: Applying security controls to protect the backend resources. Ensuring reliability. Effective monitoring and troubleshooting with tracing requests and context flow. n this blog post, I will walk you through a practical example: hosting an MCP server behind Azure API Management, configuring credential management, and connecting with GitHub Copilot. A Practical Example: Automating Issue Triage To follow along with this scenario, please check out our Model Context Protocol (MCP) lab available at AI-Gateway/labs/model-context-protocol Let's move from theory to practice by exploring how MCP, Azure API Management (APIM) and GitHub Copilot can transform a common engineering workflow. Imagine you're an engineering manager aiming to streamline your team's issue triage process - reducing manual steps and improving efficiency. Example workflow: Engineers log bugs/ feature requests as GitHub issues Following a manual review, a corresponding incident ticket is generated in ServiceNow. This manual handoff is inefficient and error prone. Let's see how we can automate this process - securely connecting GitHub and ServiceNow, enabling an AI Agent (GitHub Copilot in VS Code) to handle triage tasks on your behalf. A significant challenge in this integration involves securely managing delegated access to backend APIs, like GitHub and ServiceNow, from your MCP Server. Azure API Management's credential manager solves this by centralizing secure credential storage and facilitating the secure creation of connections to your third-party backend APIs. Build and deploy your MCP server(s) We'll start by building two MCP servers: GitHub Issues MCP Server Provides tools to authenticate on GitHub (authorize_github), retrieve user infromation (get_user ) and list issues for a specified repository (list_issues). ServiceNow Incidents MCP Server Provides tools to authenticate with ServiceNow (authorize_servicenow), list existing incidents (list_incidents) and create new incidents (create_incident). We are using Azure API Management to secure and protect both MCP servers, which are built using Azure Container Apps. Azure API Management's credential manager centralizes secure credential storage and facilitates the secure creation of connections to your backend third-party APIs. Client Auth: You can leverage API Management subscriptions to generate subscription keys, enabling client access to these APIs. Optionally, to further secure /sse and /messages endpoints, we apply the validate-jwt policy to ensure that only clients presenting a valid JWT can access these endpoints, preventing unauthorized access. (see: AI-Gateway/labs/model-context-protocol/src/github/apim-api/auth-client-policy.xml) After registering OAuth applications in GitHub and ServiceNow, we update APIM's credential manager with the respective Client IDs and Client Secrets. This enables APIM to perform OAuth flows on behalf of users, securely storing and managing tokens for backend calls to GitHub and ServiceNow. Connecting your MCP Server in VS Code With your MCP servers deployed and secured behind Azure API Management, the next step is to connect them to your development workflow. Visual Studio Code now supports MCP, enabling GitHub Copilot's agent mode to connect to any MCP-compatible server and extend its capabilities. Open Command Pallette and type in MCP: Add Server ... Select server type as HTTP (HTTP or Server-Sent Events) Paste in the Server URL Provide a Server ID This process automatically updates your settings.json with the MCP server configuration. Once added, GitHub Copilot can connect to your MCP servers and access the defined tools, enabling agentic workflows such as issue triage and automation. You can repeat these steps to add the ServiceNow MCP Server. Understanding Authentication and Authorization with Credential Manager When a user initiates an authentication workflow (e.g, via the authorize_github tool), GitHub Copilot triggers the MCP server to generate an authorization request and a unique login URL. The user is redirected to a consent page, where their registered OAuth application requests permissions to access their GitHub account. Azure API Management acts as a secure intermediary, managing the OAuth flow and token storage. Flow of authorize_github: Step 1 - Connection initiation: GitHub Copilot Agent invokes a sse connection to API Management via the MCP Client (VS Code) Step 2 - Tool Discovery: APIM forwards the request to the GitHub MCP Server, which responds with available tools Step 3 - Authorization Request: GitHub Copilot selects and executes authorize_github tool. The MCP server generates an authorization_id for the chat session. Step 4 - User Consent: If it's the 1st login, APIM requests a login redirect URL from the MCP Server The MCP Server sends the Login URL to the client, prompting the user to authenticate with GitHub Upon successful login, GitHub redirects the client with an authorization code Step 5 - Token Exchange and Storage: The MCP Client sends the authorization code to API Management APIM exchanges the code for access and refresh tokens from GitHub APIM securely stores the token and creates an Access Control List (ACL) for the service principal. Step 6 - Confirmation: APIM confirms successful authentication to the MCP Client, and the user can now perform authenticated actions, such as accessing private repositories. Check out the python logic for how to implement it: AI-Gateway/labs/model-context-protocol/src/github/mcp-server/mcp-server.py Understanding Tool Calling with underlaying APIs in API Management Using the list_issues tool, Connection confirmed APIM confirms the connection to the MCP Client Issue retrieval: The MCP Client requests issues from the MCP server The MCP Server attaches the authorization_id as a header and forwards the request to APIM The list of issues is returned to the agent You can use the same process to add the ServiceNow MCP Server. With both servers connected, GitHub Copilot Agent can extract issues from a private repo in GitHub and create new incidences in ServiceNow, automating your triage workflow. You can define additional tools such as suggest_assignee tool, assign_engineer tool, update_incident_status tool, notify_engineer tool, request_feedback tool and other to demonstrate a truly closed-loop, automated engineering workflow - from issue creation to resolution and feedback. Take a look at this brief demo showcasing the entire end-to-end process: Summary Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we demonstrated how Azure API Management's credential manager solves the secure creation of connections to your backend APIs. By integrating MCP servers with VS Code and leveraging APIM for OAuth flows and token management, you can enable secure, agenting automation across your engineering tools. This approach not only streamlines workflows like issues triage and incident creation but also ensures enterprise-grade security and governance for all APIs. Additional Resources Using Credential Manager will help with managing OAuth 2.0 tokens to backend services. Client Auth for remote MCP servers: AZD up: https://aka.ms/mcp-remote-apim-auth AI lab Client Auth: AI-Gateway/labs/mcp-client-authorization/mcp-client-authorization.ipynb Blog Post: https://aka.ms/remote-mcp-apim-auth-blog If you have any questions or would like to learn more about how MCP and Azure API Management can benefit your organization, feel free to reach out to us. We are always here to help and provide further insights. Connect with us on LinkedIn (Julia Kasper & Julia Muiruri) and follow for more updates, insights, and discussions on AI integrations and API management.
