"Information about Microsoft Azure cloud platform" matching MCP servers:

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Enables read-only querying of Azure Log Analytics and Azure Resource Graph through MCP, supporting KQL queries, workspace discovery, and resource inventory exploration with Azure RBAC authentication.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Enables Azure SQL Database performance and administration through typed execution, query benchmarking, and managed DBA operations.

Connects AI assistants directly to Azure so users can manage infrastructure, resources, costs, security, and identities through plain-English conversation using their own Entra ID/RBAC permissions.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

Enables AI agents to retrieve behavioral UX insights, including rage clicks, dead clicks, quick backs, excessive scrolling, and JavaScript runtime errors, with persistent caching and quota-aware diagnostics.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.

Inspect and manage IONOS CLOUD infrastructure via MCP

Enables MCP hosts like ChatGPT and Claude to query data platform metadata, explore schemas and tables, inspect SQL explain plans, and monitor DAG and ETL status through standardized tools.

Enables comprehensive integration with Azure AI services including OpenAI, Cognitive Services, Computer Vision, and Face API through a mission-critical MCP server. Provides enterprise-grade reliability with high availability, observability, chaos engineering, and secure multi-region deployment capabilities.

Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.

An MCP server for the Frappe Cloud control plane that lets agents manage sites, bench groups, servers, backups, deploys, logs, analytics, and billing. It provides read, write, and optional destructive tools for operating Frappe Cloud hosting infrastructure.

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Enables local or HTTP-based MCP interactions with a mock SAP Cloud ALM environment, enforcing read/write capability separation and fail-closed security policies for future SAP Cloud ALM connectivity.

An MCP server that enables interaction with Google Cloud Logging API, allowing users to write, read, and manage log entries and configurations through natural language.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

Connects MCP clients to Gemini Cloud Assist APIs, enabling users to understand, manage, and troubleshoot their Google Cloud environment using natural language.

Enables LLM clients to inspect and query your own GCP and AWS infrastructure, including BigQuery/Athena, CI build systems, container/serverless runtimes, and their logs through configurable environments.