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Case study

Internal MCP Server

Claude wired into DevOps, Microsoft 365, Azure, and ads platforms — with dry-run/confirm on every write.

MCPAzure DevOpsMicrosoft GraphAzure

At a glance

  1. ChallengeRunning a small business on Azure, Microsoft 365, Azure DevOps and two ad platforms means constant context-switching between portals for low-value questions: what's the pipeline doing, what's in that mailbox, what did that keyword cost.
  2. ApproachAn internal Model Context Protocol server that gives an AI assistant structured, authenticated, purpose-scoped access to those systems — repositories, pipelines, work items and pull requests; mail, calendar and files; Azure resources and metrics; advertising and search-performance data.
  3. OutcomeOperational questions answered in the conversation instead of a tour of portals
tools
444 tools
changes previewed and confirmed, 16 Sep to 16 Oct 2026
443 previewed-and-confirmed changes

Challenge

Running a small business on Azure, Microsoft 365, Azure DevOps and two ad platforms means constant context-switching between portals for low-value questions: what's the pipeline doing, what's in that mailbox, what did that keyword cost. Each answer is easy; getting to it isn't.

Approach

An internal Model Context Protocol server that gives an AI assistant structured, authenticated, purpose-scoped access to those systems — repositories, pipelines, work items and pull requests; mail, calendar and files; Azure resources and metrics; advertising and search-performance data. Every tool that can change something follows a dry-run/confirm protocol: the change is previewed and must be explicitly confirmed before it runs. Errors come back with their status and a remediation hint, transient failures retry automatically, and read-only tools return compact, structured data designed to be reasoned about. When a capability is missing, the assistant can raise a work item for it from inside the conversation — the server's own backlog is fed by its use.

Outcome

  • Operational questions answered in the conversation instead of a tour of portals
  • Pull requests created, reviewed, voted on and completed without leaving the conversation
  • No accidental writes — every mutation previewed and confirmed
  • Used on most days across development, infrastructure and operations work; this website's own maintenance runs through it
  • 444 tools, and 443 previewed-and-confirmed changes between 16 September and 16 October 2026 — pull requests, work items, calendar events and more

Have a similar problem in your own systems? Book a free 20-minute call or describe it.

Where judgement mattered

  • Dry-run/confirm on every write as a non-negotiable design rule, not a per-tool option
  • Narrow, purpose-scoped tools rather than one general-purpose "run anything" tool
  • Errors that tell you what to do next, because an assistant that can't recover is worse than no assistant

The same thinking runs on this website: it publishes tools an assistant can call directly rather than scrape. See the agent surface and check it yourself.

Technology stack

Model Context Protocol · C# / .NET · Azure DevOps REST APIs · Microsoft Graph · Azure Resource Manager and Monitor · Google and Microsoft advertising and search APIs

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