{"id":809,"date":"2025-05-27T16:00:00","date_gmt":"2025-05-27T23:00:00","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/foundry\/?p=809"},"modified":"2025-05-29T19:18:17","modified_gmt":"2025-05-30T02:18:17","slug":"azure-ai-foundry-mcp-server-may-2025","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/foundry\/azure-ai-foundry-mcp-server-may-2025\/","title":{"rendered":"Azure AI Foundry MCP Server May 2025 Update: Adding Models, Knowledge &amp; Evaluation"},"content":{"rendered":"<h1>Azure AI Foundry MCP Server May 2025 Update: Adding Models, Knowledge &amp; Evaluation<\/h1>\n<p>At <a href=\"https:\/\/www.youtube.com\/watch?v=NV0_vYrVvE4\">Microsoft Build 2025<\/a>, Satya Nadella highlighted the transformative potential of the Model Context Protocol (MCP) in democratizing AI development. Today, we&#8217;re excited to add more capabilities to our MCP Server for Azure AI Foundry &#8211; a powerful integration that brings this vision to life for developers working with Azure AI services.<\/p>\n<h2>The Developer Challenge We&#8217;re Solving<\/h2>\n<p><strong>Before MCP Server:<\/strong> Developers struggled with complex API integrations, inconsistent interfaces across Azure AI services, and time-consuming model exploration. Building AI applications required deep knowledge of multiple SDKs, authentication methods, and service-specific protocols.<\/p>\n<p><strong>With MCP Server:<\/strong> One unified protocol connects your favorite AI assistants directly to Azure AI Foundry&#8217;s full capabilities. Now you can explore models, manage knowledge bases, and run evaluations using natural language &#8211; just like having a conversation with your development environment.<\/p>\n<h2>What is the MCP Server for Azure AI Foundry?<\/h2>\n<p>The <a href=\"https:\/\/github.com\/azure-ai-foundry\/mcp-foundry\">MCP Server for Azure AI Foundry<\/a> (experimental) is a powerful integration layer that brings together <a href=\"https:\/\/ai.azure.com\/\">Azure AI Foundry<\/a>&#8216;s capabilities through the standardized <a href=\"https:\/\/modelcontextprotocol.io\/introduction\">Model Context Protocol<\/a>. This server acts as a bridge between large language model clients (like <a href=\"https:\/\/github.com\/features\/copilot\">GitHub Copilot<\/a> and <a href=\"https:\/\/claude.ai\/download\">Claude Desktop<\/a>) and <a href=\"https:\/\/ai.azure.com\/\">Azure AI Foundry<\/a>, providing a unified interface for AI model exploration, knowledge management, and comprehensive evaluation.<\/p>\n<p>This MCP server is provided as an example to show how developers can leverage MCP to build their own integrations with Azure AI Foundry, and to invite the community to contribute to its development.<\/p>\n<h2>Why Use the MCP Server?<\/h2>\n<p>\ud83d\ude80 <strong>Faster Development Cycles<\/strong> &#8211; Natural language commands eliminate API documentation overhead<\/p>\n<p>\ud83d\udd27 <strong>Simplified Architecture<\/strong> &#8211; Replace multiple SDK integrations with a single MCP protocol<\/p>\n<p>\ud83d\udcac <strong>Intuitive Interaction<\/strong> &#8211; Query your AI infrastructure conversationally<\/p>\n<p>\ud83c\udfaf <strong>Complete Toolkit<\/strong> &#8211; Agents, Models, knowledge management, and evaluation in one interface<\/p>\n<h2>Prerequisites &amp; Quick Setup<\/h2>\n<p>Before diving in, ensure you have:<\/p>\n<ul>\n<li><code>uv<\/code> installed (<a href=\"https:\/\/docs.astral.sh\/uv\/getting-started\/installation\/\">Installation Guide<\/a>)<\/li>\n<li>Azure subscription with appropriate permissions<\/li>\n<li>Environment variables configured (we&#8217;ll cover this below)<\/li>\n<\/ul>\n<h3>\ud83d\ude80 Fastest Way to Start: GitHub Template<\/h3>\n<p><a href=\"https:\/\/github.com\/azure-ai-foundry\/foundry-models-playground\/generate\"><img decoding=\"async\" src=\"https:\/\/img.shields.io\/static\/v1?style=for-the-badge&amp;label=Use+The+Template&amp;message=GitHub&amp;color=181717&amp;logo=github\" alt=\"Use The Template\" \/><\/a><\/p>\n<p>Create your own repo using this template and open it in GitHub Codespace. Everything is pre-configured &#8211; just open GitHub Copilot in Agent mode and start chatting.<\/p>\n<p><figure id=\"attachment_913\" aria-labelledby=\"figcaption_attachment_913\" class=\"wp-caption alignnone\" ><a href=\"https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2025\/05\/start-mcp-server-480p.gif\"><img decoding=\"async\" class=\"wp-image-913 size-full\" src=\"https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2025\/05\/start-mcp-server-480p.gif\" alt=\"Animated GIF that shows how to start the MCP Server for Azure AI Foundry\" width=\"852\" height=\"480\" \/><\/a><figcaption id=\"figcaption_attachment_913\" class=\"wp-caption-text\">How to start the MCP Server for Azure AI Foundry<\/figcaption><\/figure><\/p>\n<h3>\ud83d\udd27 VS Code Integration<\/h3>\n<p><a href=\"https:\/\/insiders.vscode.dev\/redirect\/mcp\/install?name=Azure%20Foundry%20MCP%20Server&amp;config=%7B%22type%22%3A%22stdio%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22--prerelease%3Dallow%22%2C%22--from%22%2C%22git%2Bhttps%3A%2F%2Fgithub.com%2Fazure-ai-foundry%2Fmcp-foundry.git%22%2C%22run-azure-ai-foundry-mcp%22%5D%7D\"><img decoding=\"async\" src=\"https:\/\/img.shields.io\/static\/v1?style=for-the-badge&amp;label=Install+in+VS+Code&amp;message=Open&amp;color=007ACC&amp;logo=visualstudiocode\" alt=\"Install in VS Code\" \/><\/a><\/p>\n<p>This automatically sets up the MCP server in your VS Code environment under user settings.<\/p>\n<div class=\"mceTemp\"><\/div>\n<h2>Core Capabilities: What You Can Do<\/h2>\n<h3>\ud83e\udd16 Models: Discover, Build, Deploy<\/h3>\n<p><strong>Explore the Model Catalog<\/strong><\/p>\n<ul>\n<li><code>What models can I use from Azure AI Foundry?<\/code> &#8211; <em>Discover available models in the catalog<\/em><\/li>\n<li><code>What are the most popular models in Azure AI Foundry? Pick me 10 models.<\/code> &#8211; <em>Get curated recommendations<\/em><\/li>\n<li><code>Show me models from Meta<\/code> &#8211; <em>Filter by specific publishers<\/em><\/li>\n<li><code>What models support GitHub token for free testing?<\/code> &#8211; <em>Find models for prototyping<\/em><\/li>\n<\/ul>\n<p><strong>Build Prototypes Rapidly<\/strong><\/p>\n<ul>\n<li><code>How can you help me build a prototype using the model?<\/code> &#8211; <em>Get step-by-step guidance<\/em><\/li>\n<li><code>I need to build an application that can analyze my web UX designs<\/code> &#8211; <em>Receive tailored recommendations<\/em><\/li>\n<\/ul>\n<p><strong>Deploy with Confidence<\/strong><\/p>\n<ul>\n<li><code>Can you help me deploy OpenAI models?<\/code> &#8211; <em>Get deployment guidance<\/em><\/li>\n<li><code>What steps do I need to take to deploy OpenAI models on Azure AI Foundry?<\/code> &#8211; <em>Detailed deployment workflows<\/em><\/li>\n<\/ul>\n<table style=\"width: 94.8589%; height: 201px;\">\n<thead>\n<tr>\n<th style=\"width: 9.37829%;\">Category<\/th>\n<th style=\"width: 42.4658%;\">Key Tools<\/th>\n<th style=\"width: 82.0478%;\">What They Do<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"width: 9.37829%;\"><strong>Explore<\/strong><\/td>\n<td style=\"width: 42.4658%;\"><code>list_models_from_model_catalog<\/code><\/td>\n<td style=\"width: 82.0478%;\">Browse the entire Azure AI Foundry catalog with filters<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 9.37829%;\"><strong>\u00a0<\/strong><\/td>\n<td style=\"width: 42.4658%;\"><code>list_azure_ai_foundry_labs_projects<\/code><\/td>\n<td style=\"width: 82.0478%;\">Browse SOTA projects from Azure AI Foundry Labs<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 9.37829%;\"><\/td>\n<td style=\"width: 42.4658%;\"><code>get_model_details_and_code_samples<\/code><\/td>\n<td style=\"width: 82.0478%;\">Get implementation details and sample code<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 9.37829%;\"><strong>Build<\/strong><\/td>\n<td style=\"width: 42.4658%;\"><code>get_prototyping_instructions_for_github_and_labs<\/code><\/td>\n<td style=\"width: 82.0478%;\">Complete setup guidance for development<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 9.37829%;\"><strong>Deploy<\/strong><\/td>\n<td style=\"width: 42.4658%;\"><code>deploy_model_on_ai_services<\/code><\/td>\n<td style=\"width: 82.0478%;\">Production deployment automation<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 9.37829%;\"><\/td>\n<td style=\"width: 42.4658%;\"><code>create_foundry_project<\/code><\/td>\n<td style=\"width: 82.0478%;\">Project setup and configuration<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div><\/div>\n<div>\n<p><figure id=\"attachment_919\" aria-labelledby=\"figcaption_attachment_919\" class=\"wp-caption alignnone\" ><a href=\"https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2025\/05\/1-explore-models-brief-480p.gif\"><img decoding=\"async\" class=\"size-full wp-image-919\" src=\"https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2025\/05\/1-explore-models-brief-480p.gif\" alt=\"How to explore models using MCP Server\" width=\"852\" height=\"480\" \/><\/a><figcaption id=\"figcaption_attachment_919\" class=\"wp-caption-text\">How to explore models using MCP Server<\/figcaption><\/figure><\/p>\n<\/div>\n<h3>\ud83e\udde0 Knowledge: Search and Information Management<\/h3>\n<p>Transform how you work with enterprise knowledge using Azure AI Search integration.<\/p>\n<p><strong>Index Management Made Simple<\/strong><\/p>\n<ul>\n<li><code>Show me all my search indexes<\/code> &#8211; <em>Get overview of existing indexes<\/em><\/li>\n<li><code>Create an index for customer data<\/code> &#8211; <em>Set up new search capabilities<\/em><\/li>\n<\/ul>\n<p><strong>Document Operations<\/strong><\/p>\n<ul>\n<li><code>Add customer records to the index<\/code> &#8211; <em>Bulk document uploads<\/em><\/li>\n<li><code>Find all customers from France<\/code> &#8211; <em>Natural language querying<\/em><\/li>\n<\/ul>\n<p><strong>Advanced Search Operations<\/strong><\/p>\n<ul>\n<li><code>Show me documents where signup date is March 2025<\/code> &#8211; <em>Complex filtering<\/em><\/li>\n<li><code>How many documents are in my customer index?<\/code> &#8211; <em>Analytics and insights<\/em><\/li>\n<\/ul>\n<table>\n<thead>\n<tr>\n<th>Category<\/th>\n<th>Key Tools<\/th>\n<th>Purpose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Index Management<\/strong><\/td>\n<td><code>create_index<\/code>, <code>modify_index<\/code><\/td>\n<td>Build and customize search indexes<\/td>\n<\/tr>\n<tr>\n<td><strong>Document Operations<\/strong><\/td>\n<td><code>add_document<\/code>, <code>query_index<\/code><\/td>\n<td>Manage and search your data<\/td>\n<\/tr>\n<tr>\n<td><strong>Data Sources<\/strong><\/td>\n<td><code>create_indexer<\/code>, <code>list_data_sources<\/code><\/td>\n<td>Automate data ingestion<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>\ud83d\udcca Evaluation: Measure and Improve Performance<\/h3>\n<p>Comprehensive evaluation tools for both text responses and agent behaviors.<\/p>\n<p><strong>Text Quality Evaluation<\/strong><\/p>\n<ul>\n<li><code>Evaluate my model responses for groundedness<\/code> &#8211; <em>Check factual accuracy<\/em><\/li>\n<li><code>Run fluency evaluation on my chatbot outputs<\/code> &#8211; <em>Assess response quality<\/em><\/li>\n<\/ul>\n<p><strong>Agent Performance Testing<\/strong><\/p>\n<ul>\n<li><code>Test my agent's tool-calling accuracy<\/code> &#8211; <em>Validate agent behaviors<\/em><\/li>\n<li><code>Evaluate intent resolution capabilities<\/code> &#8211; <em>Measure understanding<\/em><\/li>\n<\/ul>\n<p><strong>Risk and Safety Assessment<\/strong><\/p>\n<ul>\n<li><code>Check for potential harmful content<\/code> &#8211; <em>Safety evaluations<\/em><\/li>\n<li><code>Evaluate for bias and fairness<\/code> &#8211; <em>Responsible AI testing<\/em><\/li>\n<\/ul>\n<h2>Environment Configuration<\/h2>\n<p>Set up your environment variables in a <code>.env<\/code> file:<\/p>\n<pre><code class=\"language-bash\"># GitHub authentication (for free model testing)\r\nGITHUB_TOKEN=your_github_token\r\n\r\n# Azure AI Search (for Knowledge capabilities)\r\nAZURE_AI_SEARCH_ENDPOINT=https:\/\/your-search-service.search.windows.net\/\r\nAZURE_AI_SEARCH_API_KEY=your_api_key\r\nSEARCH_AUTHENTICATION_METHOD=api-search-key\r\n\r\n# Azure OpenAI (for Evaluation)\r\nAZURE_OPENAI_ENDPOINT=https:\/\/your-openai-endpoint.openai.azure.com\/\r\nAZURE_OPENAI_API_KEY=your_api_key\r\nAZURE_OPENAI_DEPLOYMENT=gpt-4o\r\n\r\n# Azure Project (for Agent Evaluation)\r\nAZURE_SUBSCRIPTION_ID=your_subscription_id\r\nAZURE_RESOURCE_GROUP=your_resource_group\r\nAZURE_PROJECT_NAME=your_project_name<\/code><\/pre>\n<h2>Manual Setup for Custom Configurations<\/h2>\n<ol>\n<li><strong>Install uv<\/strong> by following <a href=\"https:\/\/docs.astral.sh\/uv\/getting-started\/installation\/\">Installing uv<\/a><\/li>\n<li><strong>Create <code>.vscode\/mcp.json<\/code><\/strong> in your workspace:<\/li>\n<\/ol>\n<pre><code class=\"language-json\">{\r\n  \"servers\": {\r\n    \"mcp_foundry_server\": {\r\n      \"type\": \"stdio\",\r\n      \"command\": \"uvx\",\r\n      \"args\": [\r\n        \"--prerelease=allow\",\r\n        \"--from\",\r\n        \"git+https:\/\/github.com\/azure-ai-foundry\/mcp-foundry.git\",\r\n        \"run-azure-ai-foundry-mcp\",\r\n        \"--envFile\",\r\n        \"${workspaceFolder}\/.env\"\r\n      ]\r\n    }\r\n  }\r\n}<\/code><\/pre>\n<ol start=\"3\">\n<li><strong>Configure Environment Variables<\/strong> &#8211; Create your <code>.env<\/code> file with the configuration above<\/li>\n<li><strong>Start the Server<\/strong> &#8211; Click the Start button in VS Code or run the command manually<\/li>\n<\/ol>\n<h2>Real-World Use Cases<\/h2>\n<p><strong>\ud83d\udd2c Prototyping Phase:<\/strong> &#8220;I need to quickly test different models for my use case&#8221;\n\u2192 Compare models, get GitHub token access, and iterate rapidly<\/p>\n<p><strong>\ud83d\udd0d Development &amp; Testing:<\/strong> &#8220;I want to build a RAG application with my company docs&#8221;\n\u2192 Create vector indexes with <a href=\"https:\/\/learn.microsoft.com\/azure\/search\/search-what-is-azure-search\">Azure AI Search<\/a>, ingest documents, and test retrieval quality<\/p>\n<p><strong>\ud83d\udcca Performance Validation:<\/strong> &#8220;I need to evaluate my AI system before production&#8221;\n\u2192 Run comprehensive evaluations across multiple quality and safety metrics<\/p>\n<p><strong>\ud83d\ude80 Production Deployment:<\/strong> &#8220;I&#8217;m ready to deploy my model to serve real users&#8221;\n\u2192 Set up Azure AI Services, deploy models, and configure monitoring<\/p>\n<h2>Sample Conversation Flows<\/h2>\n<h3>Exploring and Deploying Models<\/h3>\n<pre><code>You: \"What OpenAI models are available that I can test for free?\"\r\n\r\nMCP Server: \"Here are OpenAI models supporting GitHub token for free testing:\r\n- GPT-4o-mini (best for experimentation)\r\n- GPT-3.5-turbo (cost-effective for prototypes)\r\n...\"\r\n\r\nYou: \"Show me how to deploy GPT-4o-mini to production\"\r\n\r\nMCP Server: \"I'll help you deploy GPT-4o-mini. First, let me check your quotas and guide you through the process...\"<\/code><\/pre>\n<h3>Building Knowledge Applications<\/h3>\n<pre><code>You: \"Create a search index for customer support tickets\"\r\n\r\nMCP Server: \"I'll create an optimized index for support tickets. What fields do you need to search and filter on?\"\r\n\r\nYou: \"I need to search ticket content, filter by priority and date, and retrieve customer info\"\r\n\r\nMCP Server: \"Perfect! I'll create an index with searchable content fields, filterable priority and date fields...\"<\/code><\/pre>\n<h2>Foundry Labs: <strong>Powering Prototyping with Microsoft Research Innovation<\/strong><\/h2>\n<p><a href=\"https:\/\/ai.azure.com\/labs\">Foundry Labs<\/a> models and projects bring Microsoft Research innovations just a prompt away. With the MCP Server for Azure AI Foundry, agents can now:<\/p>\n<ul>\n<li><strong>Explore <\/strong>Foundry Labs projects and surface cutting-edge Microsoft Research models like <strong>OmniParser<\/strong><strong>\u00a0V2<\/strong> (for screen parsing) and <strong>Magnetic<\/strong><strong>\u00a0One<\/strong> (for multi-agent planning).<\/li>\n<li><strong>Recommend <\/strong>the right model based on your goal &#8211; whether you&#8217;re extracting tables from PDFs or tackling multi-agent scheduling &#8211; and explain why it is a good fit.<\/li>\n<li><strong>Generate<\/strong> starter code on demand, such as \u201cBuild a cognitive-load analyzer with OmniParser V2\u201d) by pulling integration details from MCP Server and inserting them directly into a GitHub Codespaces or Visual Studio Code.<\/li>\n<li><strong>Produce<\/strong> a runnable prototype in minutes, complete with authentication and model calls already wired in.<\/li>\n<\/ul>\n<p>The result: faster experiments with specialized Microsoft Research models, fewer repetitive steps, and a smoother path from idea to working prototype.<\/p>\n<p>See the workflow end to end in our Microsoft\u00a0Build session \u201c\u2060<a href=\"https:\/\/build.microsoft.com\/en-US\/sessions\/BRK139?source=\/schedule\">Inside Azure\u00a0AI Foundry Labs: Experimenting with the Future of AI<\/a>\u201c\u2014available on demand. Watch the demo, clone the GitHub repository, and start experimenting with Foundry Labs models.<\/p>\n<h2>What&#8217;s Next: Building on This Foundation<\/h2>\n<p>The MCP Server for Azure AI Foundry represents just the beginning of our commitment to building MCP in Microsoft products. As the MCP ecosystem grows, we expect to contribute more Azure AI Foundry capabilities to this server.<\/p>\n<h2>Ready to Transform Your AI Development Workflow?<\/h2>\n<p>The future of AI development is conversational, intuitive, and democratized. The Azure AI Foundry MCP Server makes that future available today.<\/p>\n<p><strong>\ud83d\ude80 Start Building Now:<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/github.com\/azure-ai-foundry\/foundry-mcp-playground\/generate\">Use our GitHub template<\/a><\/strong> for instant setup with zero configuration<\/li>\n<li><strong><a href=\"https:\/\/discord.gg\/REmjGvvFpW\">Join our Discord community<\/a><\/strong> to connect with other developers and share your projects<\/li>\n<li><strong><a href=\"https:\/\/github.com\/azure-ai-foundry\/mcp-foundry\">Explore the complete documentation<\/a><\/strong> for advanced scenarios and customization<\/li>\n<\/ul>\n<p><strong>\ud83d\udca1 What Will You Build First?<\/strong><\/p>\n<p>Whether you&#8217;re prototyping the next breakthrough AI application, building enterprise knowledge systems, or ensuring your models meet the highest quality standards, the MCP Server for Azure AI Foundry puts the full power of Azure AI at your fingertips &#8211; through simple, natural conversation.<\/p>\n<hr \/>\n<p><em>The MCP Server for Azure AI Foundry is currently in experimental release. We welcome your feedback and contributions to help shape its future development.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Major expansion of Azure AI Foundry MCP Server with new Models, Knowledge Management, and Evaluation capabilities joining the existing Agent Services, enabling developers to interact with Azure AI through natural language.<\/p>\n","protected":false},"author":170596,"featured_media":1563,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[3,12,44,4,2],"class_list":["post-809","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-foundry","tag-ai-development","tag-azure-openai","tag-azure-ai-foundry-labs","tag-generative-ai","tag-microsoft-foundry"],"acf":[],"blog_post_summary":"<p>Major expansion of Azure AI Foundry MCP Server with new Models, Knowledge Management, and Evaluation capabilities joining the existing Agent Services, enabling developers to interact with Azure AI through natural language.<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts\/809","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/users\/170596"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/comments?post=809"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts\/809\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/media\/1563"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/media?parent=809"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/categories?post=809"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/tags?post=809"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}