🚀 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.
🧾 Automate Invoice data extraction with Logic Apps and Document Intelligence
📘 Scenario: Modernizing invoice processing with AI In many organizations, invoices still arrive as scanned documents, email attachments, or paper-based handoffs. Extracting data from these formats — invoice number, vendor, total amount, line items — often involves manual effort, custom scripts, or brittle OCR logic. This scenario demonstrates how you can use Azure Logic Apps, the new Analyze Document Details action, and Azure OpenAI to automatically convert invoice images into structured data and store them in Azure Cosmos DB. 💡 What’s new and why it matters The key enabler here is the Analyze Document Details action — now available in Logic Apps. With this action, you can: Send any document image (JPG, PNG, PDF) Receive a clean markdown-style output of all recognized content Combine that with Azure OpenAI to extract structured fields without training a custom model This simplifies what used to be a complex task: reading from invoices and inserting usable data into systems like Cosmos DB, SQL, or ERP platforms like Dynamics. 🔭 What this Logic App does With just a few built-in actions, you can turn unstructured invoice documents into structured, searchable records. Here’s what the flow looks like: 📸 Logic App Overview ✅ Pre-requisites To try this walkthrough, make sure you have the following set up: An Azure Logic Apps Standard workflow An Azure Cosmos DB for NoSQL database + container An Azure OpenAI deployment (we used gpt-4o) A Blob Storage container (where invoice files will be dropped) 💡Try it yourself 👉 Sample logic app 🧠 Step-by-Step: Inside the Logic App Here’s what each action in the Logic App does, and how it’s configured: ⚡ Trigger: When a blob is added or updated Starts the workflow when a new invoice image is dropped into a Blob container. Blob path: the name of blob container 📸 Blob trigger configuration 🔍 Read blob content Reads the raw image or PDF content to pass into the AI models. Container: invoices Blob name: dynamically fetched from trigger output response 📸 Read blob configuration 🧠 Analyze document details (✨ New!) This is the core of the scenario — and the feature we’re excited to highlight. The new “Analyze Document Details” action in Logic Apps allows you to send any document image (JPG, PNG, PDF) to Azure Document Intelligence and receive a textual markdown representation of its contents — without needing to build a custom model. 📸 Example invoice (Source: InvoiceSample) 💡 This action is ideal for scenarios where you want to extract high-quality text from messy, unstructured images — including scanned receipts, handwritten forms, or photographed documents — and immediately work with it downstream using markdown. Model: prebuilt-invoice Content: file content from blob Output: text (or markdown) block containing all detected invoice fields and layout information 📸 Analyze document details configuration ✂️ Parse document Extracts the "text" field from the Document Intelligence output. This becomes the prompt input for the next step. 📸 Parse document configuration 💬 Get chat completions This step calls your Azure OpenAI deployment (in this case, gpt-4) to extract clean, structured JSON from the text- generated earlier. System Message: You are an intelligent invoice parser. Given the following invoice text, extract the key fields as JSON. Return only the JSON in proper notation, do not add any markdown text or anything extra. Fields: invoice_number, vendor, invoice_date, due_date, total_amount, and line_items if available User Message: Uses the parsed text from the "Parse a document" step (referenced as Parsed result text in your logic app) Temperature: 0 Ensures consistent, reliable output from the model 📤 The model returns a clean JSON response, ready to be parsed and inserted into a database. 📸 Get chat completions configuration 📦 Parse JSON Converts the raw OpenAI response string into a JSON object. Use a sample schema that matches your expected invoice fields to generate a sample payload. Content: Chat completion outputs Schema: Use a sample schema that matches your expected invoice fields to generate a sample payload. 📸 Parse JSON configuration 🧱 Compose – format for Cosmos DB Use the dynamic outputs from Parse JSON action and construct the JSON body input to be passed into CosmosDB. 📸 Compose action configuration 🗃️ Create or update item Inserts the structured document into Cosmos DB. Database ID: InvoicesDB Container ID: Invoices Partition Key: @{body('Parse_JSON')?['invoice_number']} Item: @outputs('Compose') Is Upsert: true 📸 CosmosDB action configuration ✅ Test output As shown below, you’ll see a successful end-to-end run — starting from the file upload trigger, through OpenAI extraction, all the way to inserting the final structured document into Cosmos DB. 📸 Logic App workflow run output 💬 Feedback Let us know what other kinds of demos and content you would like to see in the comments.
