M365 Audit MCP server

Docs: Getting started · Architecture · Customization · Evaluation · Diagrams · FAQ

Live demo: derekgallardo01.github.io/m365-audit-mcp — sample question + tool invocation + JSON response for each of the 5 tools, regenerated on every push.

Full-page capture (all 5 tools) →

An MCP server that exposes Microsoft 365 privacy / compliance audit checks as tools any MCP client (Claude Desktop, Cursor, VS Code, custom Agent SDK builds) can call.

Default backend is a realistic mocked tenant — so the server runs anywhere, zero credentials, zero setup. The production seam swaps to Microsoft Graph without changing the tool surface.

pip install -e .
m365-audit-mcp     # runs the MCP server over stdio

python -m pytest -q     # 11 unit tests covering every tool

Stdlib-only Python except for the mcp SDK itself. No real M365 tenant required to develop against.

Run in Docker

docker build -t m365-audit-mcp .
docker run --rm m365-audit-mcp python -m pytest -q       # run tests in the image
docker run --rm -i m365-audit-mcp                        # run the server (stdio)

For Claude Desktop / Cursor integration, prefer the pip install -e . path on the host — MCP clients launch the server as a subprocess and talk to it via pipes, which is awkward through Docker. The Dockerfile is for CI, packaging, and remote-hosted deployments.

Example: production scenario

examples/tenant_health_report.py — Calls all 5 audit tools directly (skipping MCP transport) and builds an executive markdown tenant-health report with the top 3 priority actions surfaced

python examples/tenant_health_report.py

What it's for

When you're running an M365 / Copilot rollout, the questions that come up weekly are the same: "is our tenant configured correctly?", "what SharePoint documents are orphaned?", "are our Conditional Access policies actually enforcing or still in report-only?", "where is Copilot adoption stalling?"

This MCP server lets you chat them in Claude / Cursor instead of clicking through the M365 admin centre, and returns structured data the LLM can summarise, compare, or paste straight into a sign-off doc.

Tools

Tool	What it returns	Typical question
check_tenant_privacy_config	Tenant metadata + per-item verification status mirroring the m365-privacy-config checklist	"Are we configured to keep client data in-tenant?"
find_orphaned_documents	Documents with no owner OR not accessed in N days, each with a recommendation	"What's at risk to leak into Copilot grounding?"
audit_conditional_access_policies	All CA policies; flags any not in enabled state	"Are any of our policies still report-only?"
list_dlp_policies	DLP policies, optionally filtered by location	"What's covering Teams chats?"
summarize_copilot_usage	Tenant-wide or per-team Copilot adoption stats	"Where is rollout stalling?"

Each tool returns JSON-serializable dicts. The LLM consumes them and turns them into natural language for the human, or chains tool calls (e.g. "list low-adoption teams, then for each one summarize their top prompts").

How to wire into Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "m365-audit": {
      "command": "m365-audit-mcp"
    }
  }
}

Restart Claude Desktop. The five tools appear in the tool list — Claude can now answer questions like "summarize our tenant's privacy posture and flag anything outstanding" by calling check_tenant_privacy_config without any further prompting from you.

The exact same config shape works in Cursor, in custom Agent SDK builds, and in any MCP client that supports stdio transport.

Architecture

flowchart LR
    C["MCP client<br/>(Claude Desktop / Cursor / SDK)"] <-->|stdio| S["FastMCP server<br/>src/m365_audit_mcp/server.py"]
    S --> T["audit_tools.py<br/>(pure functions)"]
    T --> B{"BACKEND"}
    B -- "default" --> M["mock_data.py<br/>(realistic mocked tenant)"]
    B -.->|"production swap"| G["Microsoft Graph client<br/>(yours to implement)"]

• server.py is a thin FastMCP wrapper — registers each tool, hands off to the pure function in audit_tools.py.
• audit_tools.py does the work. Each function is testable directly without touching the MCP transport (that's how all 11 tests work).
• mock_data.py returns realistic tenant-shaped data. To swap in real Microsoft Graph, replace the BACKEND constant in audit_tools.py with a Graph client wrapper that returns the same shape. No other code changes.

Bringing it to a real tenant

The BACKEND swap point in audit_tools.py is the single integration point. A production replacement would:

1. Implement a GraphBackend class with the same attributes (TENANT, PRIVACY_CONFIG, DOCUMENTS, CONDITIONAL_ACCESS_POLICIES, etc.) but populated from Microsoft Graph queries instead of constants.
2. Use an Entra ID app registration with the minimum scopes per tool (User.Read.All, Directory.Read.All, Policy.Read.All, Reports.Read.All, Sites.Read.All).
3. Cache aggressively — Graph quotas are real, and audit queries don't need second-by-second freshness.

The mocked default lets you develop and demo the integration into an agent (Claude Desktop or otherwise) without ever touching a real tenant.

What's inside

Path	Purpose
src/m365_audit_mcp/server.py	FastMCP server registering 5 tools
src/m365_audit_mcp/audit_tools.py	Pure-function tool implementations
src/m365_audit_mcp/mock_data.py	Realistic mocked tenant data
tests/test_audit_tools.py	11 unit tests (each tool + edge cases)
pyproject.toml	Python packaging + m365-audit-mcp script entry

Companion repos

• m365-privacy-config — the human-readable checklist this MCP server mirrors
• rag-over-docs-kit — pairs naturally if you want to ground answers in your own M365 docs in addition to the audit data
• copilot-studio-support-agent — the inverse pattern: an agent INSIDE M365, vs. this server SERVING M365 data to an agent outside it