Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.
Enables interaction with Azure AI Foundry services for model exploration, deployment, and performance evaluation. It provides tools for managing knowledge bases via AI Search Service, executing fine-tuning jobs, and orchestrating AI agents through natural language.
Enables text-to-image generation and image editing using Azure AI Foundry models. Supports generating high-quality images from text descriptions and modifying existing images through natural language prompts.
Exposes backend APIs through Azure API Management as an MCP server to enable AI assistants to interact with product catalogs and order systems. It provides standardized tools for searching products, retrieving details, and managing orders via the Model Context Protocol.
Enables natural language exploration of Azure environments by generating and executing KQL queries against Azure Resource Graph. Supports multi-tenant configurations, subscription scoping, and provides direct access to Azure resource information through conversational interactions.
A server-sent events (SSE) MCP server that runs on Azure Container Apps with API key authentication, likely providing weather-related functionality based on the configuration.
全面解析Azure DevOpsMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，Azure DevOps能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。
Enables management of Azure DevOps work items including Epics, Features, User Stories, Tasks, and Bugs through natural language. Supports CRUD operations, WIQL queries, work item relationships, and retrieval of project metadata such as iterations and area paths.
A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, providing capabilities for work item management, project management, and team collaboration through natural language.
Enables interaction with Azure DevOps through Personal Access Token authentication. Supports work item management, wiki operations, project/repository listing, and build pipeline access through natural language.
A Model Context Protocol server that integrates with Azure DevOps, enabling users to query work items, access backlogs, and perform various Azure DevOps operations through a standardized interface.
Enables AI assistants to interact with Azure DevOps to manage work items, Git repositories, branches, commits, and projects through natural language commands.
Enables interaction with Azure DevOps Boards through the Model Context Protocol. Supports work item management including listing, updating status, adding comments, and retrieving prioritized cards for development workflows.
Enables interaction with Azure DevOps through Cursor chat, providing tools to manage builds, pipelines, work items, sprints, and board operations. Supports secure authentication via Personal Access Tokens and allows natural language-driven DevOps task management.
Enables interaction with Azure DevOps services including work items, repositories, pipelines, wikis, and test plans through a local MCP server that provides direct access to Azure DevOps REST APIs from your code editor.
Enables interaction with Azure DevOps services through Personal Access Token authentication. Provides 75 tools for managing work items, builds, repositories, pull requests, and other DevOps operations via both CLI and HTTP API interfaces.
A Model Context Protocol server that enables AI agents to interact with Azure DevOps wikis, providing capabilities for content search, page management, and hierarchical structure navigation.
全面解析Azure Cli MCPMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，Azure Cli MCP能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。
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Enables users to query Azure HPC/AI Kubernetes clusters for GPU node information and InfiniBand topology details through kubectl commands. Provides tools to list GPU pool nodes with their status and retrieve network topology labels for high-performance computing workloads.
