Now in Foundry: VibeVoice-ASR, MiniMax M2.5, Qwen3.5-9B
This week's Model Mondays edition features two models that have just arrived in Microsoft Foundry: Microsoft's VibeVoice-ASR, a unified speech-to-text model that handles 60-minute audio files in a single pass with built-in speaker diarisation and timestamps, and MiniMaxAI's MiniMax-M2.5, a frontier agentic model that leads on coding and tool-use benchmarks with performance comparable to the strongest proprietary models at a fraction of their cost; and Qwen's Qwen3.5-9B, the largest of the Qwen3.5 Small Series. All three represent a shift toward long-context, multi-step capability: VibeVoice-ASR processes up to an hour of continuous audio without chunking; MiniMax-M2.5 handles complex, multi-phase agentic tasks more efficiently than its predecessor—completing SWE-Bench Verified 37% faster than M2.1 with 20% fewer tool-use rounds; and Qwen3.5-9B brings multimodal reasoning on consumer hardware that outperforms much larger models. Models of the week VibeVoice-ASR Model Specs Parameters / size: ~8.3B Primary task: Automatic Speech Recognition with diarisation and timestamps Why it's interesting 60-minute single-pass with full speaker attribution: VibeVoice-ASR processes up to 60 minutes of continuous audio without chunk-based segmentation—yielding structured JSON output with start/end timestamps, speaker IDs, and transcribed content for each segment. This eliminates the speaker-tracking drift and semantic discontinuities that chunk-based pipelines introduce at segment boundaries. Joint ASR, diarisation, and timestamps in one model: Rather than running separate systems for transcription, speaker separation, and timing, VibeVoice-ASR produces all three outputs in a single forward pass. Users can also inject customized hot words—proper nouns, technical terms, or domain-specific phrases—to improve recognition accuracy on specialized content without fine-tuning. Multilingual with native code-switching: Supports 50+ languages with no explicit language configuration required and handles code-switching within and across utterances natively. This makes it suitable for multilingual meetings and international call center recordings without pre-routing audio by language. Benchmarks: On the Open ASR Leaderboard, VibeVoice-ASR achieves an average WER of 7.77% across 8 English datasets (RTFx 51.80), including 2.20% on LibriSpeech Clean and 2.57% on TED-LIUM. On the MLC-Challenge multi-speaker benchmark: DER 4.28%, cpWER 11.48%, tcpWER 13.02%. Try it Use case What to build Best practices Long-form, multi-speaker transcription for meetings + compliance A transcription service that ingests up to 60 minutes of audio per request and returns structured segments with speaker IDs + start/end timestamps + transcript text (ready for search, summaries, or compliance review). Keep audio un-chunked (single-pass) to preserve speaker coherence and avoid stitching drift; rely on the model’s joint ASR, diarisation, and timestamping so you don’t need separate diarisation/timestamp pipelines or postprocessing. Multilingual + domain-specific transcription (global support, technical reviews) A global transcription workflow for multilingual meetings or call center recordings that outputs “who/when/what,” and supports vocabulary injection for product names, acronyms, and technical terms. Provide customized hot words (names / technical terms) in the request to improve recognition on specialized content; don’t require explicit language configuration—VibeVoice-ASR supports 50+ languages and code-switching, so you can avoid pre-routing audio by language. Read more about the model and try out the playground Microsoft for Hugging Face Spaces to try the model for yourself. MiniMax-M2.5 Model Specs Parameters / size: ~229B (FP8, Mixture of Experts) Primary task: Text generation (agentic coding, tool use, search) Why it's interesting? Leading coding benchmark performance: Scores 80.2% on SWE-Bench Verified and 51.3% on Multi-SWE-Bench across 10+ programming languages (Go, C, C++, TypeScript, Rust, Python, Java, and others). In evaluations across different agent harnesses, M2.5 scores 79.7% on Droid and 76.1% on OpenCode—both ahead of Claude Opus 4.6 (78.9% and 75.9% respectively). The model was trained across 200,000+ real-world coding environments covering the full development lifecycle: system design, environment setup, feature iteration, code review, and testing. Expert-level search and tool use: M2.5 achieves industry-leading performance in BrowseComp, Wide Search, and Real-world Intelligent Search Evaluation (RISE), laying a solid foundation for autonomously handling complex tasks. Professional office work: Achieves a 59.0% average win rate against other mainstream models in financial modeling, Word, and PowerPoint tasks, evaluated via the GDPval-MM framework with pairwise comparison by senior domain professionals (finance, law, social sciences). M2.5 was co-developed with these professionals to incorporate domain-specific tacit knowledge—rather than general instruction-following—into the model's training. Try it Use case What to build Best practices Agentic software engineering Multi‑file code refactors, CI‑gated patch generation, long‑running coding agents working across large repositories Start prompts with a clear architecture or refactor goal. Let the model plan before editing files, keep tool calls sequential, and break large changes into staged tasks to maintain state and coherence across long workflows. Autonomous productivity agents Research assistants, web‑enabled task agents, document and spreadsheet generation workflows Be explicit about intent and expected output format. Decompose complex objectives into smaller steps (search → synthesize → generate), and leverage the model’s long‑context handling for multi‑step reasoning and document creation. With these use cases and best practices in mind, the next step is translating them into a clear, bounded prompt that gives the model a specific goal and the right tools to act. The example below shows how a product or engineering team might frame an automated code review and implementation task, so the model can reason through the work step by step and return results that map directly back to the original requirement: “You're building an automated code review and feature implementation system for a backend engineering team. Deploy MiniMax-M2.5 in Microsoft Foundry with access to your repository's file system tools and test runner. Given a GitHub issue describing a new API endpoint requirement, have the model first write a functional specification decomposing the requirement into sub-tasks, then implement the endpoint across the relevant service files, write unit tests with at least 85% coverage, and return a pull request summary explaining each code change and its relationship to the original requirement. Flag any implementation decisions that deviate from the patterns found in the existing codebase.” Qwen3.5-9B Model Specs Parameters / size: 9B Context length: 262,144 tokens natively; extensible to 1,010,000 tokens Primary task: Image-text-to-text (multimodal reasoning) Why it’s interesting High intelligence density at small sizes: Qwen 3.5 Small models show large reasoning gains relative to parameter count, with the 4B and 9B variants outperforming other sub‑10B models on public reasoning benchmarks. Long‑context by default: Support for up to 262K tokens enables long‑document analysis, codebase review, and multi‑turn workflows without chunking. Native multimodal architecture: Vision is built into the model architecture rather than added via adapters, allowing small models (0.8B, 2B) to handle image‑text tasks efficiently. Open and deployable: Apache‑2.0 licensed models designed for local, edge, or cloud deployment scenarios. Benchmarks AI Model & API Providers Analysis | Artificial Analysis Try it Use case When to use Best‑practice prompt pattern Long‑context reasoning Analyzing full PDFs, long research papers, or large code repositories where chunking would lose context Set a clear goal and scope. Ask the model to summarize key arguments, surface contradictions, or trace decisions across the entire document before producing an output. Lightweight multimodal document understanding OCR‑driven workflows using screenshots, scanned forms, or mixed image‑text inputs Ground the task in the artifact. Instruct the model to first describe what it sees, then extract structured information, then answer follow‑up questions. With these best practices in mind, Qwen 3.5-9B demonstrates how compact, multimodal models can handle complex reasoning tasks without chunking or manual orchestration. The prompt below shows how an operations analyst might use the model to analyze a full report end‑to‑end: "You are assisting an operations analyst. Review the attached PDF report and extracted tables. Identify the three largest cost drivers, explain how they changed quarter‑over‑quarter, and flag any anomalies that would require follow‑up. If information is missing, state what data would be needed." Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft Foundry
Introducing Phi-4-Reasoning-Vision to Microsoft Foundry
Vision reasoning models unlock a critical capability for developers: the ability to move beyond passive perception toward systems that can understand, reason over, and act on visual information. Instead of treating images, diagrams, documents, or UI screens as unstructured inputs, vision reasoning models enable developers to build applications that can interpret visual structure, connect it with textual context, and perform multi-step reasoning to reach actionable conclusions. Today, we are excited to announce Phi-4-Reasoning-Vision-15B is available in Microsoft Foundry and Hugging Face. This model brings high‑fidelity vision to the reasoning‑focused Phi‑4 family, extending small language models (SLMs) beyond perception into structured, multi‑step visual reasoning for agents, analytical tools, and scientific workflows. What’s new? The Phi model family has advanced toward combining efficient visual understanding with strong reasoning in small language models. Earlier Phi‑4 models demonstrated reliable perception and grounding across images and text, while later iterations introduced structured reasoning to improve performance on complex tasks. Phi‑4‑reasoning-vision-15B brings these threads together, pairing high‑resolution visual perception with selective, task‑aware reasoning. As a result, the model can reason deeply when needed while remaining fast and efficient for perception‑focused scenarios—making it well suited for interactive, real‑world applications. Key capabilities Reasoning behavior is explicitly enabled via prompting: Developers can explicitly enable or disable reasoning to balance latency and accuracy at runtime. Optimized for vision reasoning and can be used for: diagram-based math, document, chart, and table understanding, GUI interpretations and grounding for agent scenarios to interpret screens and actions, Computer-use agent scenarios, and General image chat and answering questions Benchmarks The following results summarize Phi-4-reasoning-vision-15B performance across a set of established multimodal reasoning, mathematics, and computer use benchmarks. The following benchmarks are the result of internal evaluations. Benchmark Phi-4-reasoning-vision-15B Phi-4-reasoning-vision-15B – force no think Phi-4-mm-instruct Kimi-VL-A3B-Instruct gemma-3-12b-it Qwen3-VL-8B-Instruct-4K Qwen3-VL-8B-Instruct-32K Qwen3-VL-32B-Instruct-4K Qwen3-VL-32B-Instruct-32K AI2D _TEST 84.8 84.7 68.6 84.6 80.4 82.7 83 84.8 85 ChartQA _TEST 83.3 76.5 23.5 87 39 83.1 83.2 84.3 84 HallusionBench 64.4 63.1 56 65.2 65.3 73.5 74.1 74.4 74.9 MathVerse _MINI 44.9 43.8 32.4 41.7 29.8 54.5 57.4 64.2 64.2 MathVision _MINI 36.2 34.2 20 28.3 31.9 45.7 50 54.3 60.5 MathVista _MINI 75.2 68.7 50.5 67.1 57.4 77.1 76.4 82.5 81.8 MMMU _VAL 54.3 52 42.3 52 50 60.7 64.6 68.6 70.6 MMStar 64.5 63.3 45.9 60 59.4 68.9 69.9 73.7 74.3 OCRBench 76 75.6 62.6 86.5 75.3 89.2 90 88.5 88.5 ScreenSpot _v2 88.2 88.3 28.5 89.8 3.5 91.5 91.5 93.7 93.9 Table 1: Accuracy comparisons relative to popular open-weight, non-thinking models Benchmark Phi-4-reasoning-vision-15B Phi-4-reasoning-vision-15B - force thinking Kimi-VL-A3B-Thinking gemma-3-12b-it Qwen3-VL-8B-Thinking-4K Qwen3-VL-8B-Thinking-40K Qwen3-VL-32B-Thiking-4K Qwen3-VL-32B-Thinking-40K AI2D_TEST 84.8 79.7 81.2 80.4 83.5 83.9 86.9 87.2 ChartQA _TEST 83.3 82.9 73.3 39 78 78.6 78.5 79.1 HallusionBench 64.4 63.9 70.6 65.3 71.6 73 76.4 76.6 MathVerse _MINI 44.9 53.1 61 29.8 67.3 73.3 78.3 78.2 MathVision _MINI 36.2 36.2 50.3 31.9 43.1 50.7 60.9 58.6 MathVista _MINI 75.2 74.1 78.6 57.4 77.7 79.5 83.9 83.8 MMMU _VAL 54.3 55 60.2 50 59.3 65.3 72 72.2 MMStar 64.5 63.9 69.6 59.4 69.3 72.3 75.5 75.7 OCRBench 76 73.7 79.9 75.3 81.2 82 83.7 85 ScreenSpot _v2 88.2 88.1 81.8 3.5 93.3 92.7 83.1 83.1 Table 2: Accuracy comparisons relative to popular open-weight, thinking models All results were obtained using a consistent evaluation setup and prompts across models; numbers are provided for comparison and analysis rather than as leaderboard claims. For more information regarding benchmarks and evaluations, please read the technical paper on the Microsoft Research hub. Suggested use cases and applications Phi‑4‑Reasoning-Vision-15B supports applications that require both high‑fidelity visual perception and structured inference. Two representative scenarios include scientific and mathematical reasoning over visual inputs, and computer‑using agents (CUAs) that operate directly on graphical user interfaces. In both cases, the model provides grounded visual understanding paired with controllable, low‑latency reasoning suitable for interactive systems. Computer use agents in retail scenarios For computer use agents, Phi‑4‑Reasoning-Vision-15B provides the perception and grounding layer required to understand and act within live ecommerce interfaces. For example, in an online shopping experience, the model interprets screen content—products, prices, filters, promotions, buttons, and cart state—and produces grounded observations that agentic models like Fara-7B can use to select actions. Its compact size and low latency inference make it well suited for CUA workflows and agentic applications. Visual reasoning for education Another practical use of visual reasoning models is education. A developer could build a K‑12 tutoring app with Phi‑4‑Reasoning‑Vision‑15B where students upload photos of worksheets, charts, or diagrams to get guided help—not answers. The model can understand the visual content, identify where the student went wrong, and explain the correct steps clearly. Over time, the app can adapt by serving new examples matched to the student’s learning level, turning visual problem‑solving into a personalized learning experience. Microsoft Responsible AI principles At Microsoft, our mission to empower people and organizations remains constant—especially in the age of AI, where the potential for human achievement is greater than ever. We recognize that trust is foundational to AI adoption, and earning that trust requires a commitment to transparency, safety, and accountability. As with other Phi models, Phi-4-Reasoning-Vision-15B was developed with safety as a core consideration throughout training and evaluation. The model was trained on a mixture of public safety datasets and internally generated examples designed to elicit behaviors the model should appropriately refuse, in alignment with Microsoft’s Responsible AI Principles. These safety focused training signals help the model recognize and decline requests that fall outside intended or acceptable use. Additional details on the model’s safety considerations, evaluation approach, and known limitations are provided in the accompanying technical blog and model card. Getting started Start using Phi‑4‑Reasoning-Vision-15B in Microsoft Foundry today. Microsoft Foundry provides a unified environment for model discovery, evaluation, and deployment, making it straightforward to move from initial experimentation to production use while applying appropriate safety and governance practices. Deploy the new model on Microsoft Foundry. Learn more about the Phi family on Foundry Labs and in the Phi Cookbook Connect to the Microsoft Developer Community on Discord Read the technical paper on Microsoft Research Read more use cases on the Educators Developer blogNew Azure Open AI models bring fast, expressive, and real‑time AI experiences in Microsoft Foundry
Modern AI applications, whether voice‑first experiences or building large software systems, rarely fit into a single prompt. Real work unfolds over time: maintaining context, following instructions, invoking tools, and adapting as requirements evolve. When these foundations break down through latency spikes, instruction drift, or unreliable tool calls, both user conversations and developer workflows are impacted. OpenAI’s latest models address this shared challenge by prioritizing continuity and reliability across real‑time interaction and long‑running engineering tasks. Starting today, GPT-Realtime-1.5, GPT-Audio-1.5, and GPT-5.3-Codex are rolling out into Microsoft Foundry. Together, these models reflect the growing needs of the modern developer and push the needle from short, stateless interactions toward AI systems that can reason, act, and collaborate over time. GPT-5.3-Codex at a glance GPT‑5.3‑Codex brings together advanced coding capability with broader reasoning and professional problem solving in a single model built for real engineering work. It unifies the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT5.2 in one system. This shifts the experience from optimizing isolated outputs to supporting longer running development efforts; where repositories are large, changes span multiple steps, and requirements aren’t always fully specified at the start. What’s improved Model experiences 25% faster execution time, according to Open AI, than its predecessors so developers can accelerate development of new applications. Built for long-running tasks that involve research, tool use, and complex, multi‑step execution while maintaining context. Midtask steerability and frequent updates allow developers to redirect and collaborate with the model as it works without losing context. Stronger computer-use capabilities allow developers to execute across the full spectrum of technical work. Common use cases Developers and teams can apply GPT‑5.3‑Codex across a wide range of scenarios, including: Refactoring and modernizing large or legacy applications Performing multi‑step migrations or upgrades Running agentic developer workflows that span analysis, implementation, testing, and remediation Automating code reviews, test generation, and defect detection Supporting development in security‑sensitive or regulated environments Pricing Model Input Price/1M Tokens Cached Input Price/1M Tokens Output Price/1M Tokens GPT-5.3-Codex $1.75 $0.175 $14.00 GPT-Realtime-1.5 and GPT-Audio-1.5 at a glance The models deliver measurable gains in reasoning and speech understanding for real‑time voice interactions on Microsoft Foundry. In OpenAI’s evaluations, it shows a +5% lift on Big Bench Audio (reasoning), a +10.23% improvement in alphanumeric transcription, and a +7% gain in instruction following, while maintaining low‑latency performance. Key improvements include: What's improved More natural‑sounding speech: Audio output is smoother and more conversational, with improved pacing and prosody. Higher audio quality: Clearer, more consistent audio output across supported voices. Improved instruction following: Better alignment with developer‑provided system and user instructions during live interactions. Function calling support: Enables structured, tool‑driven interactions within real‑time audio flows. Common use cases Developers are using GPT-Realtime-1.5 and GPT-Audio-1.5 for scenarios where low‑latency voice interaction is essential, including: Conversational voice agents for customer support or internal help desks Voice‑enabled assistants embedded in applications or devices Live voice interfaces for kiosks, demos, and interactive experiences Hands‑free workflows where audio input and output replace keyboard interaction Pricing Model Text Audio Image Input Cached Input Output Input Cached Input Output Input Cached Input Output GPT-Realtime-1.5 $4.00 $0.04 $16.0 $32.0 $0.40 $64.00 $4.00 $0.04 $16.0 GPT-Audio-1.5 $2.50 n/a $10.0 $32.00 n/a $64.00 $2.50 n/a $10.0 Getting started in Microsoft Foundry Start building in Microsoft Foundry, evaluate performance, and explore Azure Open AI models today. Foundry brings evaluation, deployment, and governance into a single workflow, helping teams progress from experiments to scalable applications while maintaining security and operational controls.Now in Foundry: Qwen3-Coder-Next, Qwen3-ASR-1.7B, Z-Image
This week's spotlight features three models from that demonstrate enterprise-grade AI across the full scope of modalities. From low latency coding agents to state-of-the-art multilingual speech recognition and foundation-quality image generation, these models showcase the breadth of innovation happening in open-source AI. Each model balances performance with practical deployment considerations, making them viable for production systems while pushing the boundaries of what's possible in their respective domains. This week's Model Mondays edition highlights Qwen3-Coder-Next, an 80B MoE model that activates only 3B parameters while delivering coding agent capabilities with 256k context; Qwen3-ASR-1.7B, which achieves state-of-the-art accuracy across 52 languages and dialects; and Z-Image from Tongyi-MAI, an undistilled text-to-image foundation model with full Classifier-Free Guidance support for professional creative workflows. Models of the week Qwen: Qwen3-Coder-Next Model Specs Parameters / size: 80B total (3B activated) Context length: 262,144 tokens Primary task: Text generation (coding agents, tool use) Why it's interesting Extreme efficiency: Activates only 3B of 80B parameters while delivering performance comparable to models with 10-20x more active parameters, making advanced coding agents viable for local deployment on consumer hardware Built for agentic workflows: Excels at long-horizon reasoning, complex tool usage, and recovering from execution failures, a critical capability for autonomous development that go beyond simple code completion Benchmarks: Competitive performance with significantly larger models on SWE-bench and coding benchmarks (Technical Report) Try it Use Case Prompt Pattern Code generation with tool use Provide task context, available tools, and execution environment details Long-context refactoring Include full codebase context within 256k window with specific refactoring goals Autonomous debugging Present error logs, stack traces, and relevant code with failure recovery instructions Multi-file code synthesis Describe architecture requirements and file structure expectations Financial services sample prompt: You are a coding agent for a fintech platform. Implement a transaction reconciliation service that processes batches of transactions, detects discrepancies between internal records and bank statements, and generates audit reports. Use the provided database connection tool, logging utility, and alert system. Handle edge cases including partial matches, timing differences, and duplicate transactions. Include unit tests with 90%+ coverage. Qwen: Qwen3-ASR-1.7B Model Specs Parameters / size: 1.7B Context length: 256 tokens (default), configurable up to 4096 Primary task: Automatic speech recognition (multilingual) Why it's interesting All-in-one multilingual capability: Single 1.7B model handles language identification plus speech recognition for 30 languages, 22 Chinese dialects, and English accents from multiple regions—eliminating the need to manage separate models per language Specialized audio versatility: Transcribes not just clean speech but singing voice, songs with background music, and extended audio files, expanding use cases beyond traditional ASR to entertainment and media workflows State-of-the-art accuracy: Outperforms GPT-4o, Gemini-2.5, and Whisper-large-v3 across multiple benchmarks. English: Tedlium 4.50 WER vs 7.69/6.15/6.84; Chinese: WenetSpeech 4.97/5.88 WER vs 15.30/14.43/9.86 (Technical Paper) Language ID included: 97.9% average accuracy across benchmark datasets for automatic language identification, eliminating the need for separate language detection pipelines Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation Customer support sample prompt: Deploy Qwen3-ASR-1.7B to a Microsoft Foundry endpoint and transcribe multilingual customer service calls. Send audio files via API to automatically detect the language (from 52 supported options including 30 languages and 22 Chinese dialects) and generate accurate transcripts. Process calls from customers speaking English, Spanish, Mandarin, Cantonese, Arabic, French, and other languages without managing separate models per language. Use transcripts for quality assurance, compliance monitoring, and customer sentiment analysis. Tongyi-MAI: Z-Image Model Specs Parameters / size: 6B Context length: N/A (text-to-image) Primary task: Text-to-image generation Why it's interesting Undistilled foundation model: Full-capacity base without distillation preserves complete training signal with Classifier-Free Guidance support (a technique that improves prompt adherence and output quality), enabling complex prompt engineering and negative prompting that distilled models cannot achieve High output diversity: Generates distinct character identities in multi-person scenes with varied compositions, facial features, and lighting, critical for creative applications requiring visual variety rather than consistency Aesthetic versatility: Handles diverse visual styles from hyper-realistic photography to anime and stylized illustrations within a single model, supporting resolutions from 512×512 to 2048×2048 at any aspect ratio with 28-50 inference steps (Technical Paper) Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation E-commerce sample prompt: Professional product photography of a modern ergonomic office chair in a bright Scandinavian-style home office. Natural window lighting from left, clean white desk with laptop and succulent plant, light oak hardwood floor. Chair positioned at 45-degree angle showing design details. Photorealistic, commercial photography, sharp focus, 85mm lens, f/2.8, soft shadows. Getting started You can deploy open‑source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryWhat is trending in Hugging Face on Microsoft Foundry? Feb, 2, 2026
Open‑source AI is moving fast, with important breakthroughs in reasoning, agentic systems, multimodality, and efficiency emerging every day. Hugging Face has been a leading platform where researchers, startups, and developers share and discover new models. Microsoft Foundry brings these trending Hugging Face models into a production‑ready experience, where developers can explore, evaluate, and deploy them within their Azure environment. Our weekly Model Monday’s series highlights Hugging Face models available in Foundry, focusing on what matters most to developers: why a model is interesting, where it fits, and how to put it to work quickly. This week’s Model Mondays edition highlights three Hugging Face models, including a powerful Mixture-of-Experts model from Z. AI designed for lightweight deployment, Meta’s unified foundation model for image and video segmentation, and MiniMax’s latest open-source agentic model optimized for complex workflows. Models of the week Z.AI’s GLM-4.7-flash Model Basics Model name: zai-org/GLM-4.7-Flash Parameters / size: 30B total -3B active Default settings: 131,072 max new tokens Primary task: Agentic, Reasoning and Coding Why this model matters Why it’s interesting: It utilizes a Mixture-of-Experts (MoE) architecture (30B total parameters and 3B active parameters) to offer a new option for lightweight deployment. It demonstrates strong performance on logic and reasoning benchmarks, outperforming similar sized models like gpt-oss-20b on AIME 25 and GPQA benchmarks. It supports advanced inference features like "Preserved Thinking" mode for multi-turn agentic tasks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: From the Foundry catalog, users can deploy on a A100 instance or unsloth/GLM-4.7-Flash-GGUF on a CPU. ource SOTA scores among models of comparable size. Additionally, compared to similarly sized models, GLM-4.7-Flash demonstrates superior frontend and backend development capabilities. Click to see more: https://docs.z.ai Try it Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑context agent workflows (local or low‑cost autonomous agents) Call out long‑horizon consistency and context preservation. Instruct the model to retain earlier assumptions and decisions across turns. Now that you know GLM‑4.7‑Flash works best when you give it a clear goal and let it reason through a bounded task, here’s an example prompt that a product or engineering team might use to identify risks and propose mitigations: You are a software reliability analyst for a mid‑scale SaaS platform. Review recent incident reports, production logs, and customer issues to uncover edge‑case failures outside normal usage (e.g., rare inputs, boundary conditions, timing/concurrency issues, config drift, or unexpected feature interactions). Prioritize low‑frequency, high‑impact risks that standard testing misses. Recommend minimal, low‑cost fixes (validation, guardrails, fallback logic, or documentation). Deliver a concise executive summary with sections: Observed Edge Cases, Root Causes, User Impact, Recommended Lightweight Fixes, and Validation Steps. Meta's Segment Anything 3 (SAM3) Model Basics Model name: facebook/sam3 Parameters / size: 0.9B Primary task: Mask Generation, Promptable Concept Segmentation (PCS) Why this model matters Why it’s interesting: It handles a vastly larger set of open-vocabulary prompts than SAM 2, and unifies image and video segmentation capabilities. It includes a "SAM 3 Tracker" mode that acts as a drop-in replacement for SAM 2 workflows with improved performance. Best‑fit use cases: Open-vocabulary object detection, video object tracking, and automatic mask generation What’s notable: Introduces Promptable Concept Segmentation (PCS), allowing users to find all matching objects (e.g., "dial") via text prompt rather than just single instances. Try it This model enables users to identify specific objects within video footage and isolate them over extended periods. With just one line of code, it is possible to detect multiple similar objects simultaneously. The accompanying GIF demonstrates how SAM3 efficiently highlights players wearing white on the field as they appear and disappear from view. Additional examples are available at the following repository: https://github.com/facebookresearch/sam3/blob/main/assets/player.gif Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat SAM 3 as a concept detector, not an interactive click tool. Use short, concrete noun‑phrase concept prompts instead of describing the scene or asking questions. Example prompt: “yellow school bus” or “shipping containers”. Avoid verbs or full sentences. Video segmentation + object tracking Specify the same concept prompt once, then apply it across the video sequence. Do not restate the prompt per frame. Let the model maintain identity continuity. Example: “person wearing a red jersey”. Hard‑to‑name or visually subtle objects Use exemplar‑based prompts (image region or box) when text alone is ambiguous. Optionally combine positive and negative exemplars to refine the concept. Avoid over‑constraining with long descriptions. Using the GIF above as a leading example, here is a prompt that shows how SAM 3 turns raw sports footage into structured, reusable data. By identifying and tracking players based on visual concepts like jersey color so that sports leagues can turn tracked data into interactive experiences where automated player identification can relay stats, fun facts, etc when built into a larger application. Here is a prompt that will allow you to start identifying specific players across video: Act as a sports analytics operator analyzing football match footage. Segment and track all football players wearing blue jerseys across the video. Generate pixel‑accurate segmentation masks for each player and assign persistent instance IDs that remain stable during camera movement, zoom, and player occlusion. Exclude referees, opposing team jerseys, sidelines, and crowd. Output frame‑level masks and tracking metadata suitable for overlays, player statistics, and downstream analytics pipelines. MiniMax AI's MiniMax-M2.1 Model Basics Model name: MiniMaxAI/MiniMax-M2.1 Parameters / size: 229B-10B Active Default settings: 200,000 max new tokens Primary task: Agentic and Coding Why this model matters Why it’s interesting: It is optimized for robustness in coding, tool use, and long-horizon planning, outperforming Claude Sonnet 4.5 in multilingual scenarios. It excels in full-stack application development, capable of architecting apps "from zero to one”. Previous coding models focused on Python optimization, M2.1 brings enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The model delivers exceptional stability across various coding agent frameworks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: The release of open-source weights for M2.1 delivers a massive leap over M2 on software engineering leaderboards. https://www.minimax.io/ Try it Use case Best‑practice prompt pattern End‑to‑end agentic coding (multi‑file edits, run‑fix loops) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑horizon tool‑using agents (shell, browser, Python) Explicitly request stepwise planning and sequential tool use. M2.1’s interleaved thinking and improved instruction‑constraint handling are designed for complex, multi‑step analytical tasks that require evidence tracking and coherent synthesis, not conversational back‑and‑forth. Long‑context reasoning & analysis (large documents / logs) Declare the scope and desired output structure up front. MiniMax‑M2.1 performs best when the objective and final artifact are clear, allowing it to manage long context and maintain coherence. Because MiniMax‑M2.1 is designed to act as a long‑horizon analytical agent, it shines when you give it a clear end goal and let it work through large volumes of information—here’s a prompt a risk or compliance team could use in practice: You are a financial risk analysis agent. Analyze the following transaction logs and compliance policy documents to identify potential regulatory violations and systemic risk patterns. Plan your approach before executing. Work through the data step by step, referencing evidence where relevant. Deliver a final report with the following sections: Key Risk Patterns Identified, Supporting Evidence, Potential Regulatory Impact, Recommended Mitigations. Your response should be a complete, executive-ready report, not a conversational draft. Getting started You can deploy open‑source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryPublishing Agents from Microsoft Foundry to Microsoft 365 Copilot & Teams
Better Together is a series on how Microsoft’s AI platforms work seamlessly to build, deploy, and manage intelligent agents at enterprise scale. As organizations embrace AI across every workflow, Microsoft Foundry, Microsoft 365, Agent 365, and Microsoft Copilot Studio are coming together to deliver a unified approach—from development to deployment to day-to-day operations. This three-part series explores how these technologies connect to help enterprises build AI agents that are secure, governed, and deeply integrated with Microsoft’s product ecosystem. Series Overview Part 1: Publishing from Foundry to Microsoft 365 Copilot and Microsoft Teams Part 2: Foundry + Agent 365 — Native Integration for Enterprise AI Part 3: Microsoft Copilot Studio Integration with Foundry Agents This blog focuses on Part 1: Publishing from Foundry to Microsoft 365 Copilot—how developers can now publish agents built in Foundry directly to Microsoft 365 Copilot and Teams in just a few clicks. Build once. Publish everywhere. Developers can now take an AI agent built in Microsoft Foundry and publish it directly to Microsoft 365 Copilot and Microsoft Teams in just a few clicks. The new streamlined publishing flow eliminates manual setup across Entra ID, Azure Bot Service, and manifest files, turning hours of configuration into a seamless, guided flow in the Foundry Playground. Simplifying Agent Publishing for Microsoft 365 Copilot & Microsoft Teams Previously, deploying a Foundry AI agent into Microsoft 365 Copilot and Microsoft Teams required multiple steps: app registration, bot provisioning, manifest editing, and admin approval. With the new Foundry → M365 integration, the process is straightforward and intuitive. Key capabilities No-code publishing — Prepare, package, and publish agents directly from Foundry Playground. Unified build — A single agent package powers multiple Microsoft 365 channels, including Teams Chat, Microsoft 365 Copilot Chat, and BizChat. Agent-type agnostic — Works seamlessly whether you have a prompt agent, hosted agent, or workflow agent. Built-in Governance — Every agent published to your organization is automatically routed through Microsoft 365 Admin Center (MAC) for review, approval, and monitoring. Downloadable package — Developers can download a .zip for local testing or submission to the Microsoft Marketplace. For pro-code developers, the experience is also simplified. A C# code-first sample in the Agent Toolkit for Visual Studio is searchable, featured, and ready to use. Why It Matters This integration isn’t just about convenience; it’s about scale, control, and trust. Faster time to value — Deliver intelligent agents where people already work, without infrastructure overhead. Enterprise control — Admins retain full oversight via Microsoft 365 Admin Center, with built-in approval, review and governance flows. Developer flexibility — Both low-code creators and pro-code developers benefit from the unified publishing experience. Better Together — This capability lays the groundwork for Agent 365 publishing and deeper M365 integrations. Real-world scenarios YoungWilliams built Priya, an AI agent that helps handle government service inquiries faster and more efficiently. Using the one-click publishing flow, Priya was quickly deployed to Microsoft Teams and M365 Copilot without manual setup. This allowed Young Williams’ customers to provide faster, more accurate responses while keeping governance and compliance intact. “Integrating Microsoft Foundry with Microsoft 365 Copilot fundamentally changed how we deliver AI solutions to our government partners,” said John Tidwell, CTO of YoungWilliams. “With Foundry’s one-click publishing to Teams and Copilot, we can take an idea from prototype to production in days instead of weeks—while maintaining the enterprise-grade security and governance our clients expect. It’s a game changer for how public services can adopt AI responsibly and at scale.” Availability Publishing from Foundry to M365 is in Public Preview within the Foundry Playground. Developers can explore the preview in Microsoft Foundry and test the Teams / M365 publishing flow today. SDK and CLI extensions for code-first publishing are generally available. What’s Next in the Better Together Series This blog is part of the broader Better Together series connecting Microsoft Foundry, Microsoft 365, Agent 365, and Microsoft Copilot Studio. Continue the journey: Foundry + Agent 365 — Native Integration for Enterprise AI (Link) Start building today [Quickstart — Publish an Agent to Microsoft 365 ] Try it now in the new Foundry Playground
OptiMind: A small language model with optimization expertise
Turning a real world decision problem into a solver ready optimization model can take days—sometimes weeks—even for experienced teams. The hardest part is often not solving the problem; it’s translating business intent into precise mathematical objectives, constraints, and variables. OptiMind is designed to try and remove that bottleneck. This optimization‑aware language model translates natural‑language problem descriptions into solver‑ready mathematical formulations, can help organizations move from ideas to decisions faster. Now available through public preview as an experimental model through Microsoft Foundry, OptiMind targets one of the more expertise‑intensive steps in modern optimization workflows. Addressing the Optimization Bottleneck Mathematical optimization underpins many enterprise‑critical decisions—from designing supply chains and scheduling workforces to structuring financial portfolios and deploying networks. While today’s solvers can handle enormous and complex problem instances, formulating those problems remains a major obstacle. Defining objectives, constraints, and decision variables is an expertise‑driven process that often takes days or weeks, even when the underlying business problem is well understood. OptiMind tries to address this gap by automating and accelerating formulation. Developed by Microsoft Research, OptiMind transforms what was once a slow, error‑prone modeling task into a streamlined, repeatable step—freeing teams to focus on decision quality rather than syntax. What makes OptiMind different? OptiMind is not just as a language model, but as a specialized system built for real-world optimization tasks. Unlike general-purpose large language models adapted for optimization through prompting, OptiMind is purpose-built for mixed integer linear programming (MILP), and its design reflects this singular focus. At inference time, OptiMind follows a multi‑stage process: Problem classification (e.g., scheduling, routing, network design) Hint retrieval tailored to the identified problem class Solution generation in solver‑compatible formats such as GurobiPy Optional self‑correction, where multiple candidate formulations are generated and validated This design can improve reliability without relying on agentic orchestration or multiple large models. In internal evaluations on cleaned public benchmarks—including IndustryOR, Mamo‑Complex, and OptMATH—OptiMind demonstrated higher formulation accuracy than similarly sized open models and competitive performance relative to significantly larger systems. OptiMind improved accuracy by approximately 10 percent over the base model. In comparison to open-source models under 32 billion parameters, OptiMind was also found to match or exceed performance benchmarks. For more information on the model, please read the official research blog or the technical paper for OptiMind. Practical use cases: Unlocking efficiency across domains OptiMind is especially valuable where modeling effort—not solver capability—is the primary bottleneck. Typical use cases include: Supply Chain Network Design: Faster formulation of multi‑period network models and logistics flows Manufacturing and Workforce Scheduling: Easier capacity planning under complex operational constraints Logistics and Routing Optimization: Rapid modeling that captures real‑world constraints and variability Financial Portfolio Optimization: More efficient exploration of portfolios under regulatory and market constraints By reducing the time and expertise required to move from problem description to validated model, OptiMind helps teams reach actionable decisions faster and with greater confidence. Getting started OptiMind is available today as an experimental model, and Microsoft Research welcomes feedback from practitioners and enterprise teams. Next steps: Explore the research details: Read more about the model on Foundry Labs and the technical paper on arXiv Try the model: Access OptiMind through Microsoft Foundry Test sample code: Available in the OptiMind GitHub repository Take the next step in optimization innovation with OptiMind—empowering faster, more accurate, and cost-effective problem solving for the future of decision intelligence.Open AI’s GPT-5.1-codex-max in Microsoft Foundry: Igniting a New Era for Enterprise Developers
Announcing GPT-5.1-codex-max: The Future of Enterprise Coding Starts Now We’re thrilled to announce the general availability of OpenAI's GPT-5.1-codex-max in Microsoft Foundry Models; a leap forward that redefines what’s possible for enterprise-grade coding agents. This isn’t just another model release; it’s a celebration of innovation, partnership, and the relentless pursuit of developer empowerment. At Microsoft Ignite, we unveiled Microsoft Foundry: a unified platform where businesses can confidently choose the right model for every job, backed by enterprise-grade reliability. Foundry brings together the best from OpenAI, Anthropic, xAI, Black Forest Labs, Cohere, Meta, Mistral, and Microsoft’s own breakthroughs, all under one roof. Our partnership with Anthropic is a testament to our commitment to giving developers access to the most advanced, safe, and high-performing models in the industry. And now, with GPT-5.1-codex-max joining the Foundry family, the possibilities for intelligent applications and agentic workflows have never been greater. GPT 5.1-codex-max is available today in Microsoft Foundry and accessible in Visual Studio Code via the Foundry extension . Meet GPT-5.1-codex-max: Enterprise-Grade Coding Agent for Complex Projects GPT-5.1-codex-max is engineered for those who build the future. Imagine tackling complex, long-running projects without losing context or momentum. GPT-5.1-codex-max delivers efficiency at scale, cross-platform readiness, and proven performance with top scores on SWE-Bench (77.9), the gold standard for AI coding. With GPT-5.1-codex-max, developers can focus on creativity and problem-solving, while the model handles the heavy lifting. GPT-5.1-codex-max isn’t just powerful; it’s practical, designed to solve real challenges for enterprise developers: Multi-Agent Coding Workflows: Automate repetitive tasks across microservices, maintaining shared context for seamless collaboration. Enterprise App Modernization: Effortlessly refactor legacy .NET and Java applications into cloud-native architectures. Secure API Development: Generate and validate secure API endpoints, with `compliance checks built-in for peace of mind. Continuous Integration Support: Integrate GPT-5.1-codex-max into CI/CD pipelines for automated code reviews and test generation, accelerating delivery cycles. These use cases are just the beginning. GPT-5.1-codex-max is your partner in building robust, scalable, and secure solutions. Foundry: Platform Built for Developers Who Build the Future Foundry is more than a model catalog—it’s an enterprise AI platform designed for developers who need choice, reliability, and speed. • Choice Without Compromise: Access the widest range of models, including frontier models from leading model providers. • Enterprise-Grade Infrastructure: Built-in security, observability, and governance for responsible AI at scale. • Integrated Developer Experience: From GitHub to Visual Studio Code, Foundry connects with tools developers love for a frictionless build-to-deploy journey. Start Building Smarter with GPT-5.1-codex-max in Foundry The future is here, and it’s yours to shape. Supercharge your coding workflows with GPT-5.1-codex-max in Microsoft Foundry today. Learn more about Microsoft Foundry: aka.ms/IgniteFoundryModels. Watch Ignite sessions for deep dives and demos: ignite.microsoft.com. Build faster, smarter, and with confidence on the platform redefining enterprise AI.Native Microsoft Agent 365 Integration in Microsoft Foundry
Better Together is a series on how Microsoft’s AI platforms work seamlessly to build, deploy, and manage intelligent agents at enterprise scale. As organizations embrace AI across every workflow, Microsoft Foundry, Microsoft 365, Microsoft Agent 365, and Microsoft Copilot Studio are coming together to deliver a unified approach—from development to deployment to day-to-day operations. This three-part series explores how these technologies connect to help enterprises build AI agents that are secure, governed, and deeply integrated with Microsoft’s product ecosystem. This blog focuses on Part 2: Microsoft Foundry + A365Microsoft Agent 365 native Integration, showing how organizations can build, deploy, and customize Microsoft Agent 365 agents directly from Foundry. What Is Microsoft Agent 365? Microsoft Agent 365 is the control plane for enterprise AI agents, allowing IT to register, secure, and scale agents across Microsoft 365 and third-party environments. AI agents act more like people than code—they bring skills, learn from context, and leverage enterprise data to complete tasks. Like with people in the enterprise, they need to be protected from digital threats, governed with the right IT controls, and managed following enterprise policies. Our philosophy is simple: treat agents like users. Extend your existing identity, security, compliance, and productivity infrastructure to agents using familiar tools adapted for their unique needs. Each agent receives its own identity, policies, and access controls, ensuring it operates effectively while staying compliant. With Agent 365, organizations can: Manage AI agents at scale with unified identity and lifecycle controls Enforce least-privilege access and compliance with Defender, Entra, and Purview Boost productivity through native integration with Microsoft 365 apps and Work IQ Monitor activity and apply policies from a single, secure registry Learn more about Microsoft Agent 365 Foundry: The Ideal Place for Developers to Build AI Agents Microsoft Foundry is the ideal platform for building, testing, and deploying Agent 365 agents. It provides a unified environment where developers can create enterprise-ready AI agents that are secure, governed, and fully integrated with Microsoft 365. At Ignite, Foundry introduces support for Agent 365 hosted (containerized) agents, giving developers a consistent, scalable runtime managed entirely within the Microsoft cloud. This initial release focuses on hosted agents to provide a fully managed and secure environment from development to deployment. With Foundry, developers can: Author agents quickly using low-code or pro-code workflows Test and iterate in a secure, hosted environment Integrate frontier AI models from Microsoft, OpenAI, Meta, DeepSeek, and xAI Package and deploy agents with Microsoft identity, security, and governance built in Through its native integration with Microsoft Agent 365, Foundry also provides: Foundry-hosted runtime for seamless agent execution Azure Bot Service and Microsoft 365 app integration (Teams, Outlook, M365 Copilot) MCP-connected tools from Microsoft Agent 365 Simplified preparation flow for publishing to M365 Copilot, Teams and BizChat Apps Together, Foundry and Microsoft Agent 365 let organizations build, host, and manage AI agents natively, making them enterprise-ready from day one. What Can Employees Do with Agent 365? With Agent 365, employees can: Automate email triage and meeting preparation Summarize and generate content Locate organizational knowledge instantly Orchestrate cross-system workflows and approvals Advanced teams can also: Integrate internal knowledge bases Create business-specific workflows Extend actions using Foundry APIs and connectors Why It Matters This integration makes Agent 365 agents enterprise-ready out of the box—combining the authoring power of Microsoft Foundry with the security and manageability of the Microsoft 365 ecosystem. IT retains control over policy, compliance, and lifecycle management, while business users gain intelligent agents that work across the tools they already use. Get Started Early access to Microsoft Agent 365 is available through the Frontier preview program, offering hands-on experience with Microsoft’s latest AI innovations. 🔗 [Quickstart — Publish an Agent to A365 GitHub Sample]Implementing MCP Remote Servers with Azure Function App and GitHub Copilot Integration
Introduction In the evolving landscape of AI-driven applications, the ability to seamlessly connect large language models (LLMs) with external tools and data sources is becoming a cornerstone of intelligent system design. Model Context Protocol (MCP) — a specification that enables AI agents to discover and invoke tools dynamically, based on context. While MCP is powerful, implementing it from scratch can be daunting !!! That’s where Azure Functions comes in handy. With its event-driven, serverless architecture, Azure Functions now supports a preview extension for MCP, allowing developers to build remote MCP servers that are scalable, secure, and cloud-native. Further, In VS Code, GitHub Copilot Chat in Agent Mode can connect to your deployed Azure Function App acting as an MCP server. This connection allows Copilot to leverage the tools and services exposed by your function app. Why Use Azure Functions for MCP? Serverless Simplicity: Deploy MCP endpoints without managing infrastructure. Secure by Design: Leverage HTTPS, system keys, and OAuth via EasyAuth or API Management. Language Flexibility: Build in .NET, Python, or Node.js using QuickStart templates. AI Integration: Enable GitHub Copilot, VS Code, or other AI agents to invoke your tools via SSE endpoints. Prerequisites Python version 3.11 or higher Azure Functions Core Tools >= 4.0.7030 Azure Developer CLI To use Visual Studio Code to run and debug locally: Visual Studio Code Azure Functions extension An storage emulator is needed when developing azure function app in VScode. you can deploy Azurite extension in VScode to meet this requirement. Press enter or click to view image in full size You can run the Azurite in VS Code as shown below. C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\IDE\Extensions\Microsoft\Azure Storage Emulator> .\azurite.exe Press enter or click to view image in full size alternatively, you can also run Azurite in docker container as shown below. docker run -p 10000:10000 -p 10001:10001 -p 10002:10002 \ mcr.microsoft.com/azure-storage/azurite For more information about setting up Azurite, visit Use Azurite emulator for local Azure Storage development | Microsoft Learn Github Repositories Following Github repos are needed to setup this PoC. Repository for MCP server using Azure Function App https://github.com/mafzal786/mcp-azure-functions-python.git Repository for AI Foundry agent as MCP Client https://github.com/mafzal786/ai-foundry-agent-with-remote-mcp-using-azure-functionapp.git Clone the repository Run the following command to clone the repository to start building your MCP server using Azure function app. git clone https://github.com/mafzal786/mcp-azure-functions-python.git Run the MCP server in VS Code Once cloned. Open the folder in VS Code. Create a virtual environment in VS Code. Change directory to “src” in a new terminal window, install the python dependencies and start the function host locally as shown below. cd src pip install -r requirements.txt func start Note: by default this will use the webhooks route: /runtime/webhooks/mcp/sse. Later we will use this in Azure to set the key on client/host calls: /runtime/webhooks/mcp/sse?code=<system_key> Press enter or click to view image in full size MCP Inspector In a new terminal window, install and run MCP Inspector. npx @modelcontextprotocol/inspector Click to load the MCP inspector. Also provide the generated proxy session token. http://127.0.0.1:6274/#resources In the URL type and click “Connect”: http://localhost:7071/runtime/webhooks/mcp/sse Once connected, click List Tools under Tools and select “hello_mcp” tool and click “Run Tool” for testing as shown below. Press enter or click to view image in full size Select another tool such as get_stockprice and run it as shown below. Press enter or click to view image in full size Deploy Function App to Azure from VS Code For deploying function app to azure from vs code, make sure you have Azure Tools extension enabled in VS Code. To learn more about Azure Tools extension, visit the following Azure Extensions if your VS code environment is not setup for Azure development, follow Configure Visual Studio Code for Azure development with .NET — .NET | Microsoft Learn Once Azure Tools are setup, sign in to Azure account with Azure Tools Press enter or click to view image in full size Once Sign-in is completed, you should be able to see all of your existing resources in the Resources view. These resources can be managed directly in VS Code. Look for Function App in Resource, right click and click “Deploy to Function App”. Press enter or click to view image in full size If you already have it deployed, you will get the following pop-up. Click “Deploy” Press enter or click to view image in full size This will start deploying your function app to Azure. In VS Code, Azure tab will display the following. Press enter or click to view image in full size Once the deployment is completed, you can view the function app and all the tools in Azure portal under function app as shown below. Press enter or click to view image in full size Get the mcp_extension key from Functions → App Keys in Function App. Press enter or click to view image in full size This mcp_extension key would be needed in mcp.json file in VS code, if you would like to test the MCP server using Github Copilot in VS Code. Your entries in mcp.json file will look like as below for example. { "inputs": [ { "type": "promptString", "id": "functions-mcp-extension-system-key", "description": "Azure Functions MCP Extension System Key", "password": true }, { "type": "promptString", "id": "functionapp-name", "description": "Azure Functions App Name" } ], "servers": { "remote-mcp-function": { "type": "sse", "url": "https://${input:functionapp-name}.azurewebsites.net/runtime/webhooks/mcp/sse", "headers": { "x-functions-key": "${input:functions-mcp-extension-system-key}" } }, "local-mcp-function": { "type": "sse", "url": "http://0.0.0.0:7071/runtime/webhooks/mcp/sse" } } } Test Azure Function MCP Server in MCP Inspector Launch MCP Inspector and provide the Azure Function in MCP inspector URL. Provide authentication as shown below. Bearer token is mcp_extension key. Testing an MCP server with GitHub Copilot Testing an MCP server with GitHub Copilot involves configuring and utilizing the server within your development environment to provide enhanced context and capabilities to Copilot Chat. Steps to Test an MCP Server with GitHub Copilot: Ensure Agent Mode is Enabled: Open Copilot Chat in Visual Studio Code and select “Agent” mode. This mode allows Copilot to interact with external tools and services, including MCP servers. Add the MCP Server: Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and run the command MCP: Add Server. Press enter or click to view image in full size Follow the prompts to configure the server. You can choose to add it to your workspace settings (creating a .vscode/mcp.json file) . Select HTTP or Server-Sent events Press enter or click to view image in full size Specify the URL and click Enter Press enter or click to view image in full size Provide a name of your choice Press enter or click to view image in full size Select scope as Global or workspace. I selected Workspace Press enter or click to view image in full size This will generate mcp.json file in .vscode or create a new entry if mcp.json already exists as shown below. Click Start to “start” the server. Also make sure your Azure function app is locally running with func start command. Press enter or click to view image in full size Now Type the prompt as shown below. Press enter or click to view image in full size Try another tool as below. Press enter or click to view image in full size VS code terminal output for reference. Press enter or click to view image in full size Testing an MCP server with Claude Desktop Claude Desktop is a standalone AI application that allows users to interact with Claude AI models directly from their desktop, providing a seamless and efficient experience. you can download Claude desktop at Download Claude In this article, I have added another tool to utilize to test your MCP server running in Azure Function app. Modify claude_desktop_config.json with the following. you can find this file in window environment at C:\Users\<username>\AppData\Roaming\Claude { "mcpServers": { "my mcp": { "command": "npx", "args": [ "mcp-remote", "http://localhost:7071/runtime/webhooks/mcp/sse" ] } } } Note: If claude_desktop_config.json does not exists, click on setting in Claude desktop under user and visit developer tab. You will see you MCP server in Claude Desktop as shown below. Press enter or click to view image in full size Type the prompt such as “What is the stock price of Tesla” . After submitting, you will notice that it is invoking the tool “get_stockprice” from the MCP server running locally and configured in the .json earlier. Click Allow once or Allow always as shown below. Following output will be displayed. Press enter or click to view image in full size Now lets try weather related prompt. As you can see, it has invoked “get_weatheralerts” tool from MCP server. Press enter or click to view image in full size Azure AI Foundry agent as MCP Client Use the following Github repo to set up Azure AI Foundry agent as MCP client. git clone https://github.com/mafzal786/ai-foundry-agent-with-remote-mcp-using-azure-functionapp.git Open the code in VS code and follow the instructions mentioned in README.md file at Github repo. Once you execute the code, following output will show up in VS code. Press enter or click to view image in full size In this code, message is hard coded. Change the content to “what is weather advisory for Florida” and rerun the program. It will call get_weatheralerts tool and output will look like as below. Press enter or click to view image in full size Conclusion The integration of Model Context Protocol (MCP) with Azure Functions marks a pivotal step in democratizing AI agent development. By leveraging Azure’s serverless architecture, developers can now build remote MCP servers that scale automatically, integrate seamlessly with other Azure services, and expose modular tools to intelligent agents like GitHub Copilot. This setup not only simplifies the deployment and management of MCP servers but also enhances the developer experience — allowing tools to be invoked contextually by AI agents in environments like VS Code, GitHub Codespaces, or Copilot Studio[2]. Whether you’re building a tool to query logs, calculate metrics, or manage data, Azure Functions provides the flexibility, security, and scalability needed to bring your AI-powered workflows to life. As the MCP spec continues to evolve, and GitHub Copilot expands its agentic capabilities, this architecture positions you to stay ahead — offering a robust foundation for cloud-native AI tooling that’s both powerful and future-proof.
