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UpdateAdminClient MCP Server

The UpdateAdminClient API, provided as part of Microsoft's Azure Update Management platform, is a powerful RESTful service designed for granular control and visibility over software update operations within an Azure subscription.

Quick Start Summary

The UpdateAdminClient MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the UpdateAdminClient API through natural language. It exposes 5 API endpoints as callable tools, such as UpdateRuns_ListTopLevel, UpdateRuns_GetTopLevel, UpdateRuns_List, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/azure-com-azsadmin-updateruns. This integration is sourced from the auto UpdateAdminClient OpenAPI specification (v2016-05-01) and has a quality score of 28/99 (fair documentation coverage).

5Endpointstools mapped
NoneAuthopen access
28/99Qualityfair
~30 secSetupno auth

Server Details

Category
Developer Tools
Authentication
None
Endpoints
5 operations
Transport
STDIO
Spec Version
v2016-05-01
Install Command
npx -y @mcp/azure-com-azsadmin-updateruns

Environment Variables

UPDATEADMINCLIENT_API_KEY

Example: your_updateadminclient_api_key

Top Endpoints

GET
/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Update.Admin/updateLocations/{updateLocation}/updateRuns

UpdateRuns_ListTopLevel

GET
/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Update.Admin/updateLocations/{updateLocation}/updateRuns/{runName}

UpdateRuns_GetTopLevel

GET
/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Update.Admin/updateLocations/{updateLocation}/updates/{updateName}/updateRuns

UpdateRuns_List

GET
/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Update.Admin/updateLocations/{updateLocation}/updates/{updateName}/updateRuns/{runName}

UpdateRuns_Get

POST
/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Update.Admin/updateLocations/{updateLocation}/updates/{updateName}/updateRuns/{runName}/rerun

UpdateRuns_Rerun

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

Capabilities & Use Cases
The UpdateAdminClient API, provided as part of Microsoft's Azure Update Management platform, is a powerful RESTful service designed for granular control and visibility over software update operations within an Azure subscription. Its core capability is the management and monitoring of "update runs," which represent the execution instances of a defined update deployment across a fleet of machines. This API enables administrators and automation systems to query, inspect, and re-initiate these update runs, providing essential operational control for maintaining compliance, applying critical patches, and managing system stability across hybrid cloud environments. Typical use cases are prominent in enterprise IT operations, DevOps pipelines, and site reliability engineering, where teams need to programmatically verify the completion status of scheduled updates, diagnose failures for specific machines, audit historical update activities, or manually trigger a re-run of an update for a subset of devices without redeploying the entire configuration.
🤖AI Agent Value
Exposing the UpdateAdminClient API as a set of tools through a Model Context Protocol (MCP) server unlocks significant value for AI coding assistants and agents. This integration transforms the AI from a passive code generator into an active, context-aware operational partner. An AI agent equipped with these tools can directly interact with the live update infrastructure, bridging the gap between development-time planning and runtime operations. For instance, a developer can ask the AI to "check if last night's security update deployment succeeded for all production servers," and the agent can use the API tools to fetch the relevant update runs, parse the results, and provide a concise report. This direct access eliminates the need for the developer to manually construct and execute complex CLI commands or navigate the Azure portal, dramatically accelerating troubleshooting, auditing, and compliance verification tasks within the development workflow.
💬Example Workflows
In practice, an AI agent can perform dynamic, multi-step workflows based on natural language instructions. A developer could instruct, "List all failed update runs for the 'ContosoWeb' resource group in the 'eastus' location from the past week, then re-run the update named 'CriticalPatch2024' for any run that failed due to a timeout." The agent would sequentially use the list endpoints to gather data, analyze the failure reasons, and then invoke the rerun endpoint to remediate the specific issues. Another example is, "Generate a summary of all update runs initiated for the 'WindowsServer2022' update last Tuesday, including success rates per machine," where the AI would aggregate data from multiple API calls to produce a structured report. These scenarios demonstrate how the API, when toolified for an AI, can automate repetitive operational queries, perform intelligent analysis of update compliance, and even execute corrective actions, freeing developers to focus on higher-level architectural tasks.
🛡️Security & Auth
While the specification indicates "None" for authentication in this context, it is critical to understand that in a real-world deployment, this API is secured via Azure Active Directory (now Microsoft Entra ID) and governed by Azure Role-Based Access Control (RBAC). Developers configuring an MCP server to use these tools must ensure they handle authentication securely, typically using managed identities or service principals with the minimum required permissions. A best practice is to assign the custom role "Update Management Reader" for read-only monitoring tasks, or "Update Management Contributor" only if the AI agent needs the ability to trigger reruns, adhering strictly to the principle of least privilege. Configuration should involve storing credentials in a secure vault (like Azure Key Vault) and never embedding them in code or configuration files. Furthermore, all API calls should be monitored, and access should be restricted to authorized AI agent identities to maintain a robust security posture.

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