Overview of the Test1 MCP Server for Microsoft Graph

The Test1 MCP server bridges Claude, or any other AI assistant that understands the Model Context Protocol, with Microsoft Graph. By exposing a single endpoint that forwards authenticated Graph requests, the server solves the common problem of securely exposing corporate data to generative AI while keeping all credentials and access control within a controlled, auditable service. Developers can therefore give their assistants the ability to read user directories, calendar events, or file metadata without embedding Azure AD secrets in the assistant’s codebase.

At its core, the server receives a JSON payload describing a Graph API call—endpoint, HTTP method, query parameters, and body—and forwards it to Azure AD‑authenticated Graph. The header identifies the caller, enabling per‑user throttling or fine‑grained permission checks. The design keeps the Graph logic out of the assistant, so the AI only needs to know what it wants (e.g., “list the first five users”) and delegates the actual API plumbing to the MCP server. This separation of concerns is valuable for teams that must comply with security policies, audit requirements, or data‑exposure limits.

Key capabilities of the Test1 server include:

• Unified Graph access: A single endpoint handles all Graph operations, simplifying client code and reducing surface area for misconfiguration.
• User‑level isolation: The header allows the server to enforce per‑user scopes, ensuring that a user can only query resources they are permitted to see.
• Extensibility: The server’s request‑forwarding logic can be extended to add caching, rate limiting, or custom logging without touching the AI side.
• Plug‑and‑play integration: The accompanying Claude application demonstrates how to wire the MCP into an AI workflow, exposing a endpoint that forwards user messages and optional system prompts to Claude while automatically attaching Graph capabilities.

Typical real‑world scenarios include:

• HR assistants that can answer questions like “How many employees are in the Marketing team?” by querying and .
• IT support bots that fetch device inventory or policy compliance data from Graph and present it to users in natural language.
• Productivity tools that integrate calendar events or OneDrive files into conversational interfaces, allowing users to schedule meetings or retrieve documents without leaving the chat.

In practice, developers set up the MCP server once, configure Azure AD permissions (e.g., ), and then integrate it into their AI stack. The AI client sends a structured request; the server validates the user ID, authenticates with Azure AD, and returns the Graph response. This workflow keeps sensitive credentials out of the assistant, satisfies compliance mandates, and provides a clear audit trail of every Graph call made on behalf of an AI conversation.