mcp-usage-monitoring

name

mcp-usage-monitoring

description

Use this skill when asked to monitor, audit, or analyze MCP (Model Context Protocol) server usage in the environment. Triggers on keywords like "MCP usage", "MCP server monitoring", "MCP activity", "Graph MCP", "Sentinel MCP", "Azure MCP", "AI agent monitoring", "MCP audit", "tool usage monitoring", "MCP breakdown", "who is using MCP", or when investigating AI agent access patterns, Graph API calls from MCP servers, or workspace query governance. This skill provides comprehensive MCP server telemetry analysis across Graph MCP, Sentinel MCP, and Azure MCP servers including usage trends, endpoint access patterns, user attribution, sensitive API detection, workspace query governance, and security risk assessment with inline and markdown file reporting.

MCP Server Usage Monitoring — Instructions

Purpose

This skill monitors and audits Model Context Protocol (MCP) server usage across your Microsoft Sentinel and Defender XDR environment. MCP servers are AI-powered tools that enable language models to interact with Microsoft security services — and like any privileged access channel, they require monitoring.

What this skill tracks:

MCP Server	Telemetry Source	Key Identifier
Microsoft Graph MCP Server	MicrosoftGraphActivityLogs	AppId = e8c77dc2-69b3-43f4-bc51-3213c9d915b4
Sentinel Data Lake MCP	CloudAppEvents	RecordType 403, Interface = IMcpToolTemplate
Sentinel Triage MCP	MicrosoftGraphActivityLogs + SigninLogs	AppId = 7b7b3966-1961-47b5-b080-43ca5482e21c ("Microsoft Defender Mcp") — dedicated AppId with full user attribution via delegated cert auth
Azure MCP Server	AzureActivity	No dedicated AppId — uses DefaultAzureCredential
Sentinel Data Lake — Direct KQL	CloudAppEvents	RecordType 379, Operation = KQLQueryCompleted
Workspace Query Sources (Analytics Tier)	LAQueryLogs	All clients querying Log Analytics workspace

What this skill detects:

• Graph API call volume, trends, and endpoint diversity via MCP
• Sensitive/high-risk Graph endpoint access (PIM, credentials, Identity Protection)
• Sentinel workspace query patterns by client application
• User vs. Service Principal vs. Agent Identity attribution across all MCP channels
• Entra Agent ID detection — distinguishes AI agent identities from standard SPNs and human users (see Agent Identity Detection)
• Azure ARM operations potentially originating from Azure MCP Server
• Non-MCP platform query sources for governance context (Sentinel Engine, Logic Apps)
• Sentinel Data Lake MCP tool usage — tool call breakdown (query_lake, list_sentinel_workspaces, search_tables, etc.), success/failure rates, execution duration, tables accessed via CloudAppEvents (Purview unified audit)
• MCP-driven vs Direct KQL delineation — distinguishes Data Lake queries initiated via MCP tools (RecordType 403, Interface IMcpToolTemplate) from direct KQL queries (RecordType 379) and Analytics tier queries (LAQueryLogs)
• Anomalous access patterns: new users, new endpoints, volume spikes, error surges
• MCP server usage as a proportion of total workspace activity

Extended landscape awareness: Beyond these four actively monitored MCP servers, Microsoft's MCP ecosystem includes 30+ additional servers (Copilot Studio built-in catalog, Power BI, Fabric RTI, Playwright, Security Copilot Agent Creation, and more). See Extended Microsoft MCP Server Landscape for the full catalog, telemetry surfaces, and monitoring expansion priorities.

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📑 TABLE OF CONTENTS

1. Critical Workflow Rules - Start here!
2. Agent Identity Detection - Entra Agent ID vs SPN vs User
3. Extended MCP Server Landscape - Full Microsoft MCP ecosystem catalog
4. Output Modes - Inline chat vs. Markdown file
5. Scalability & Token Management - Guidance for large environments
6. Quick Start - 8-step investigation pattern
7. MCP Usage Score Formula - Composite health & risk scoring
8. Execution Workflow - Complete 6-phase process
9. Sample KQL Queries - Validated query patterns
10. Report Template - Output format specification
11. Proactive Alerting — KQL Data Lake Jobs - Scheduled anomaly detection
12. Known Pitfalls - Edge cases and false positives
13. Error Handling - Troubleshooting guide

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⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Before starting ANY MCP usage monitoring analysis:

1. ALWAYS enforce Sentinel workspace selection (see Workspace Selection section below)
2. ALWAYS ask the user for output mode if not specified: inline chat summary or markdown file report (or both)
3. ALWAYS ask the user for time range if not specified: default to 30 days, configurable
4. ALWAYS query all MCP telemetry surfaces — do not skip any MCP server type
5. ALWAYS include non-MCP workspace context (Sentinel Engine, Logic Apps) for governance proportion analysis
6. ALWAYS run independent queries in parallel for performance
7. ALWAYS attribute activity to specific users — never present anonymous aggregates
8. NEVER conflate non-MCP platform activity with MCP activity — clearly label categories
9. ALWAYS check for Agent Identity (Entra Agent ID) callers — distinguish AI agents from human users and standard SPNs (see Agent Identity Detection)
10. ALWAYS execute pre-authored queries from Sample KQL Queries EXACTLY as written — substitute only the time range parameter (e.g., ago(30d) → ago(90d)). These queries encode mitigations for schema pitfalls documented in Known Pitfalls. Writing equivalent queries from scratch is ❌ PROHIBITED

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Agent Identity Detection

Microsoft Entra Agent ID (public preview) introduces a new first-class identity type for AI agents, distinct from traditional service principals and user accounts. MCP servers may be called by human users, standard SPNs, or Agent Identities — and the telemetry to distinguish them varies by table.

Reference: What are agent identities | Agent sign-in and audit logs | Agent OAuth protocols

Entra Identity Taxonomy

Identity Category	Types	MCP Usage Pattern
Human identities	User accounts (workforce, external)	User signs into VS Code → MCP server acts on behalf of user (delegated)
Workload identities	App registrations, service principals, managed identities	Standard SPN authenticates to MCP (client credentials / app-only)
🆕 Agent identities	Agent Identity, Agent Identity Blueprint, Agent User	AI agent uses MCP via Agent OBO, Autonomous App, or Agent User flow

Agent ID Object Types

Object	What It Is	Analogy
Agent Identity Blueprint	Reusable template defining a class of agent (e.g., "SOC Triage Agent")	Like an app registration
Agent Identity Blueprint Principal	SPN created when a blueprint is instantiated in a tenant	Like the SPN for an app registration
Agent Identity	Special SPN with subtype agent — represents a single agent instance	Like a service principal, but agent-specific
Agent User	Optional user account paired 1:1 with an agent identity (for mailbox, Teams, etc.)	Like a user account, but non-human

Architectural detail: Agent identities are modeled as single-tenant service principals with an "agent" subtype. The objectId and appId always have the same value (unlike regular app registrations where they differ).

Agent Authentication Flows

Flow	Pattern	When Used	Telemetry Impact
Agent On-Behalf-Of (OBO)	Delegated — agent acts on behalf of a signed-in user	User-initiated agents, copilots	UserId = human user, ServicePrincipalId = agent SPN
Autonomous App Flow	App-only — agent acts under its own authority (client_credentials)	Background agents, scheduled tasks	ServicePrincipalId = agent SPN, UserId empty
Agent User Flow	Agent uses its paired agent user account	Agents needing mailbox, Teams access	UserId = Agent User (non-human with UPN)

How to Detect Agent Identities in Telemetry

⚠️ Known Limitation (Preview): MicrosoftGraphActivityLogs does NOT yet distinguish Agent IDs from regular SPNs. Requests from agent identities appear as regular applications with the agent identity in the AppId column. Requests from agent users appear as regular users with the agent user ID in UserId.

Detection Strategy:

Telemetry Source	Detection Method	Reliability	Status
Graph API /beta tags	Query /beta/servicePrincipals/{id}?$select=tags — check for AgenticApp, AIAgentBuilder, AgentCreatedBy:CopilotStudio tags	🟢 Most reliable	✅ Available now
AuditLogs	Agent lifecycle CRUD operations — filter OperationName has "agent" or InitiatedBy = Power Virtual Agents Service	🟢 High	✅ Available now
AADServicePrincipalSignInLogs	Agent sign-ins to resources like Bot Framework from Azure internal IPs (fd00:*)	🟢 High	✅ Available now
MicrosoftGraphActivityLogs	Join with sign-in logs to identify agent callers by AppId/ServicePrincipalId	🟡 Requires cross-ref	⚠️ Workaround needed
SigninLogs	New agentSignIn resource type; filter by agent/agentType	🟡 Preview-dependent	✅ Available in preview
Graph API servicePrincipalType	GET /servicePrincipals?$filter=servicePrincipalType eq 'Agent'	🔴 Not yet reliable — Copilot Studio agents still show "Application"	⚠️ Preview gap

🔵 Field-tested finding: The servicePrincipalType property does NOT yet show "Agent" for Copilot Studio-created agents — they still return "Application" on both /v1.0 and /beta. The tags array on /beta is currently the only reliable programmatic indicator. Key tags: AgenticApp, AIAgentBuilder, AgentCreatedBy:CopilotStudio, AgenticInstance.

Sign-in log filters for agents:

• Portal: Filter by Agent type = Agent Identity or Agent ID user and Is Agent = Yes
• Graph API: $filter=signInEventTypes/any(t: t eq 'servicePrincipal') and agent/agentType eq 'AgentIdentity'

MicrosoftGraphActivityLogs — User vs SPN vs Agent Attribution

The MicrosoftGraphActivityLogs table has two identity fields that determine "who" is calling Graph APIs:

Field	Populated When	Identity Type
UserId	A human user (or Agent User) made the request via delegated permissions	User / Agent User
ServicePrincipalId	A service principal (or Agent Identity) made the request via app-only permissions	SPN / Agent Identity
ClientAuthMethod	Always populated — indicates how the caller authenticated	0 = public client (user), 1 = client secret (SPN), 2 = certificate (SPN)
Roles	Populated when app-only (application permissions)	App-only flow indicator
Scopes	Populated when delegated (user context)	Delegated flow indicator

Decision tree for caller attribution:

IF ServicePrincipalId is populated AND UserId is empty:
   → App-only flow: SPN or Agent Identity is the autonomous caller
   → Check ClientAuthMethod: 1 = secret, 2 = certificate
   → Cross-reference ServicePrincipalId with Entra to determine if Agent Identity

IF UserId is populated AND ServicePrincipalId is populated:
   → Delegated flow: User (or Agent User) signed in, SPN is the client app
   → Agent OBO: Agent acts on behalf of user — both fields populated

IF UserId is populated AND ServicePrincipalId is empty:
   → Direct user-delegated flow (e.g., VS Code → Graph MCP)
   → Check if UserId maps to an Agent User (non-human account with UPN)

Products Already Using Agent ID

Product	Agent ID Usage	Telemetry Fingerprint (Field-Tested)
Copilot Studio agents	Each created agent gets an SPN with agentic tags automatically. Initiated by Power Virtual Agents Service. Your user gets added as owner.	AuditLogs: InitiatedBy = Power Virtual Agents Service, TargetType = ServicePrincipal. SPN sign-ins to Bot Framework from Azure internal IPv6 (fd00:34f2:*). Tags: AgenticApp, AgentCreatedBy:CopilotStudio, AgenticInstance
Entra CA Optimization Agent	Microsoft first-party agent identity	May appear in tenant sign-in logs
Custom AI agents	Developers can register agents via Agent Identity Blueprint	Will appear as SPNs with agent subtype

⚠️ Name ambiguity warning: An SPN named with "Agent" in its display name does NOT mean it's an Agent Identity. Example: "Contoso Agent Tools" is a standard GitCreatedApp with servicePrincipalType: Application and no agentic tags. Always verify via /beta tags — never rely on display name alone.

Impact on This Skill

When running MCP usage monitoring:

1. Phase 1 (Graph MCP): Check BOTH UserId and ServicePrincipalId in Query 1 results — if ServicePrincipalId is populated with a non-empty value, an SPN or Agent Identity is calling Graph MCP autonomously
2. New Phase (Agent Detection): Run Query 17 to identify Agent Identities in the tenant and cross-reference against MCP callers
3. Report section: Include a dedicated "Agent vs User Attribution" breakdown showing how many MCP calls originated from humans, standard SPNs, and Agent Identities
4. Security flag: Agent Identities calling sensitive Graph endpoints autonomously (without user OBO context) should be flagged 🟠 — verify authorization

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Known AppIds Reference

MCP Servers & AI Agents

AppId	Service	Telemetry Table	Notes
e8c77dc2-69b3-43f4-bc51-3213c9d915b4	Microsoft Graph MCP Server for Enterprise	MicrosoftGraphActivityLogs	Read-only Graph API proxy
7b7b3966-1961-47b5-b080-43ca5482e21c	Sentinel Triage MCP ("Microsoft Defender Mcp")	MicrosoftGraphActivityLogs, SigninLogs, AADNonInteractiveUserSignInLogs	Microsoft first-party AppId, same across all tenants. Dedicated AppId — visible in MicrosoftGraphActivityLogs (API calls to /security/* endpoints) and SigninLogs/AADNonInteractiveUserSignInLogs (AppDisplayName = "Microsoft Defender Mcp"). Delegated auth with certificate (ClientAuthMethod=2), full user attribution. Scopes: SecurityAlert.Read.All, SecurityIncident.Read.All, ThreatHunting.Read.All. Target resources: Microsoft Graph, WindowsDefenderATP. No local SPN — display name only visible in SigninLogs. 🔴 Confirmed Feb 2026: Empirical telemetry investigation identified 7b7b3966 as the Triage MCP AppId via MicrosoftGraphActivityLogs + SigninLogs correlation.
253895df-6bd8-4eaf-b101-1381ec4306eb	Sentinel Platform Services App Reg	SigninLogs	Sentinel-hosted MCP platform
04b07795-8ddb-461a-bbee-02f9e1bf7b46	Azure MCP Server (local stdio via DefaultAzureCredential → Azure CLI)	SigninLogs, AADNonInteractiveUserSignInLogs, LAQueryLogs	Shared AppId with Azure CLI. In LAQueryLogs, RequestClientApp is empty (not a unique fingerprint). Azure MCP appends \n| limit N to query text — the only query-level differentiator. Read-only ARM ops don't appear in AzureActivity. 🔄 Updated Feb 2026: Previously documented as AppId 1950a258 (AzurePowerShellCredential) with csharpsdk,LogAnalyticsPSClient — that fingerprint is obsolete; only 1 occurrence found in 30-day lookback.
(none — uses DefaultAzureCredential)	Azure MCP Server (local stdio)	AzureActivity	ARM write operations only; read ops not logged. Claims.appid = 04b07795. Inherits cred from Azure CLI/VS Code
(no AppId — Purview unified audit)	Sentinel Data Lake MCP	CloudAppEvents	RecordType 403; Interface IMcpToolTemplate; tools: query_lake, list_sentinel_workspaces, search_tables

Sentinel MCP Collection Endpoints

Endpoint URL	Collection	Monitored
https://sentinel.microsoft.com/mcp/data-exploration	Data Exploration (Data Lake MCP)	✅ Phase 3
https://sentinel.microsoft.com/mcp/triage	Triage (Triage MCP)	✅ Phase 2
https://sentinel.microsoft.com/mcp/security-copilot-agent-creation	Security Copilot Agent Creation	❌ See Landscape

Client Applications

AppId	Service	Telemetry Table	Notes
aebc6443-996d-45c2-90f0-388ff96faa56	Visual Studio Code	SigninLogs	VS Code as MCP client → Sentinel
9ba5f2e4-6bbf-4df2-b19b-7f1bcb926818	PowerPlatform-sentinelmcp-Connector	SigninLogs	Copilot Studio → Sentinel MCP
04b07795-8ddb-461a-bbee-02f9e1bf7b46	Azure CLI (DefaultAzureCredential)	SigninLogs, AADNonInteractiveUserSignInLogs, LAQueryLogs	Primary Azure MCP Server credential path (field-tested Feb 2026). RequestClientApp is empty in LAQueryLogs. Azure MCP appends \n| limit N to query text. Shared AppId with manual az CLI — disambiguate via query text pattern or session correlation. 🔄 Previously documented as 1950a258 (AzurePowerShellCredential) — that path is obsolete

Portal & Platform Applications (Non-MCP — for context)

AppId	Service	Telemetry Table	Notes
80ccca67-54bd-44ab-8625-4b79c4dc7775	M365 Security & Compliance Center (Sentinel Portal)	LAQueryLogs	ASI_Portal, ASI_Portal_Connectors — Sentinel Portal backend, NOT an MCP server
95a5d94c-a1a0-40eb-ac6d-48c5bdee96d5	Azure Portal — AppInsightsPortalExtension	LAQueryLogs	Azure Portal blade for Log Analytics Usage dashboards/workbooks. RequestClientApp = AppInsightsPortalExtension. Executes billing/usage queries (e.g., Usage | where IsBillable). NOT MCP, NOT VS Code — runs when user opens Workspace Usage Dashboard in browser. No SPN or app registration in tenant (platform-level first-party app). Not in merill/microsoft-info known apps list.
de8c33bb-995b-4d4a-9d04-8d8af5d59601	PowerPlatform-AzureMonitorLogs-Connector	AADNonInteractiveUserSignInLogs, LAQueryLogs	Logic Apps → Log Analytics (NOT MCP)
fc780465-2017-40d4-a0c5-307022471b92	Sentinel Engine (analytics rules, UEBA, Advanced Hunting backend)	LAQueryLogs	Built-in scheduled query engine (NOT MCP). Also serves as the execution backend for Advanced Hunting — RequestClientApp = "M365D_AdvancedHunting" indicates AH queries from Triage MCP, Defender portal, or Security Copilot that hit connected LA tables (see Query 7b). Separate from analytics rules (RequestClientApp empty or other values).

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Extended Microsoft MCP Server Landscape (Reference)

Beyond the four MCP servers actively monitored by this skill, Microsoft's MCP ecosystem includes many additional servers. This section catalogs them for awareness, threat modeling, and future monitoring expansion.

Sentinel MCP Collections (Microsoft-Hosted)

Microsoft Sentinel exposes three official MCP collections, each at a distinct endpoint:

Collection	Endpoint URL	Purpose	Monitored by This Skill
Data Exploration	https://sentinel.microsoft.com/mcp/data-exploration	query_lake, search_tables, list_sentinel_workspaces, entity analyzer	✅ Phase 3 (CloudAppEvents)
Triage	https://sentinel.microsoft.com/mcp/triage	Incident triage, Advanced Hunting, entity investigation	✅ Phase 2 (MicrosoftGraphActivityLogs + SigninLogs — AppId 7b7b3966)
Security Copilot Agent Creation	https://sentinel.microsoft.com/mcp/security-copilot-agent-creation	Create Microsoft Security Copilot agents for complex workflows	❌ Not yet monitored

Sentinel Custom MCP Tools: Organizations can create their own MCP tools by exposing saved KQL queries from Advanced Hunting as MCP tools. These execute through the same Sentinel MCP infrastructure and are audited in CloudAppEvents (RecordType 403) alongside built-in tools. See Create custom Sentinel MCP tools.

🔵 Monitoring note: Custom MCP tools appear in CloudAppEvents with the same RecordType 403 and IMcpToolTemplate interface as built-in tools. The ToolName field will show the custom tool name, making them visible in Query 21 without modification.

Power BI MCP Servers

Server	Type	Endpoint / Repo	Purpose	Telemetry Surface
Power BI Remote MCP	Microsoft-hosted	https://api.fabric.microsoft.com/v1/mcp/powerbi	Query Power BI datasets, reports, and workspaces remotely via SSE transport	🟡 PowerBIActivity table (if ingested into Sentinel), Fabric audit logs
Power BI Modeling MCP	Local (stdio)	microsoft/powerbi-modeling-mcp	Local Power BI model operations (DAX queries, schema exploration)	❌ Local only — no Azure telemetry

⚠️ Data exfiltration risk: Power BI Remote MCP provides API-based access to organizational datasets. If an AI agent connects to this endpoint, it can query sensitive business data. Monitor PowerBIActivity for unusual access patterns if this table is available in your Sentinel workspace.

Fabric & Azure Data Explorer MCP Servers

Server	Type	Endpoint / Repo	Purpose	Telemetry Surface
Fabric RTI MCP Server	Local (stdio)	microsoft/fabric-rti-mcp	Query Azure Data Explorer clusters and Fabric Real-Time Intelligence Eventhouses via KQL	🟡 ADX audit logs, Fabric audit events
Azure MCP Server — Kusto namespace	Local (stdio)	Part of Azure MCP Server (azmcp --namespace kusto)	Manage ADX clusters, databases, tables, and queries via ARM	✅ Already covered (Azure ARM operations — Phase 4)
Kusto Query MCP	Copilot Studio built-in	Copilot Studio catalog	KQL query execution from Copilot Studio agents	🟡 CloudAppEvents (Copilot Studio workload)

🔵 Note: The Fabric RTI MCP Server is open-source and runs locally. It authenticates to ADX/Eventhouse using the user's credentials. If your org uses ADX, queries from this MCP would appear in ADX audit logs (.show queries / diagnostic logs), NOT in Sentinel LAQueryLogs.

Developer & Productivity MCP Servers

Server	Type	Repo	Purpose	Telemetry Surface
Playwright MCP	Local (stdio)	microsoft/playwright-mcp (26.9k ⭐)	Browser automation via accessibility tree — enables LLMs to interact with web pages	❌ Local only — no Azure telemetry
GitHub MCP Server	Local (stdio)	github/github-mcp-server	GitHub repo operations (issues, PRs, code search) via PAT	❌ GitHub audit logs only, not in Sentinel
Microsoft Learn Docs MCP	Cloud-hosted	Certified Copilot Studio connector	Search and fetch official Microsoft Learn documentation	❌ Public docs, no security data

Copilot Studio Built-in MCP Servers (19+ servers)

Microsoft Copilot Studio provides a catalog of built-in MCP servers for agent development. These are Microsoft-managed, cloud-hosted servers that agents can connect to.

Source: Built-in MCP servers catalog

Category	MCP Servers	Security Relevance
Microsoft 365	Outlook Mail, Outlook Calendar, 365 User Profile, Teams, Word, 365 Copilot (Search)	🔴 High — email, calendar, user profile access
SharePoint & OneDrive	SharePoint and OneDrive, SharePoint Lists	🟠 Medium — file and data access
Administration	365 Admin Center	🔴 High — administrative control plane
Dataverse	Dataverse MCP	🟠 Medium — business data access
Dynamics 365	Sales, Finance, Supply Chain, Service, ERP, Contact Center (6 sub-variants)	🟡 Low-Medium — business application data
Fabric	Fabric MCP	🟠 Medium — analytics data access
Office 365 Outlook	Contact Management, Email Management, Meeting Management	🔴 High — email and contact data
Meta-Server	MCP Management MCP	🟠 Medium — manages other MCP servers via Dataverse/Graph

⚠️ Telemetry gap: Copilot Studio built-in MCP servers are NOT directly visible in LAQueryLogs or MicrosoftGraphActivityLogs. Their activity may appear in:

• CloudAppEvents — under Copilot Studio workload (if Purview unified audit is configured)
• M365 unified audit log — as Copilot Studio agent actions
• AuditLogs — agent identity lifecycle events (creation, modification via Power Virtual Agents Service)
• AADServicePrincipalSignInLogs — agent SPN sign-ins to Bot Framework from Azure internal IPs (fd00:*)

To monitor Copilot Studio agent activity touching these MCP servers, use the Agent Identity Detection workflow (Phase 6) to identify agent SPNs, then trace their sign-in and audit activity.

Azure MCP Server — Full Tool Surface

The Azure MCP Server (already tracked in Phase 4) has a much broader tool surface than just ARM operations. The complete namespace catalog:

Category	Namespaces	Security-Relevant Tools
AI & ML	foundry, search, speech	AI Foundry model access, Search index queries
Identity	role	⚠️ RBAC role assignments — view and manage
Security	keyvault, appconfig, confidentialledger	🔴 Key Vault secrets/keys/certs, App Configuration
Databases	cosmos, mysql, postgres, redis, sql	Database access and management
Storage	storage, fileshares, storagesync, managedlustre	Blob, file, and storage account access
Compute	appservice, functionapp, aks	App Service, Functions, Kubernetes
Networking	eventhubs, servicebus, eventgrid, communication, signalr	Messaging and event services
DevOps	bicepschema, deploy, monitor, workbooks, grafana	Infrastructure deployment, monitoring
Governance	policy, quota, resourcehealth, cloudarchitect	Policy management, resource health
Other	marketplace, virtualdesktop, loadtesting, acr	VDI, container registry, load testing

🔵 Key Vault access via MCP is particularly security-sensitive. The Azure MCP Server implements elicitation (user confirmation prompts) before returning secrets. However, this can be bypassed with the --insecure-disable-user-confirmation flag. Monitor AzureActivity for Key Vault operations correlated with MCP usage patterns.

Monitoring Expansion Priorities

If expanding this skill's coverage, prioritize based on data access risk:

Priority	Server	Why	How to Monitor
🔴 P1	Copilot Studio built-in M365 MCPs	Email, Teams, admin center access	Agent Identity Detection (Phase 6) + CloudAppEvents
🔴 P1	Security Copilot Agent Creation	Creates autonomous security agents	CloudAppEvents for agent creation events
🟠 P2	Power BI Remote MCP	Dataset query access via API	PowerBIActivity table if available
🟠 P2	Sentinel Custom MCP Tools	User-defined tools, same audit surface	Already visible in Phase 3 CloudAppEvents
🟡 P3	Fabric RTI MCP	ADX/Eventhouse data access	ADX diagnostic logs
🟡 P3	Kusto Query MCP (Copilot Studio)	KQL from Copilot Studio agents	CloudAppEvents (Copilot Studio workload)
⚪ P4	Playwright, GitHub, Learn Docs MCPs	Local/public, minimal telemetry	Not monitorable from Sentinel

Note: This catalog reflects the Microsoft MCP ecosystem as of February 2026. The Copilot Studio MCP catalog notes: "This list isn't exhaustive. New MCP connectors are added regularly."

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⛔ MANDATORY: Sentinel Workspace Selection

This skill requires a Sentinel workspace to execute queries. Follow these rules STRICTLY:

When invoked from another skill (e.g., incident-investigation):

• Inherit the workspace selection from the parent investigation context
• If no workspace was selected in parent context: STOP and ask user to select

When invoked standalone (direct user request):

1. ALWAYS call list_sentinel_workspaces MCP tool FIRST
2. If 1 workspace exists: Auto-select, display to user, proceed
3. If multiple workspaces exist:
   • Display all workspaces with Name and ID
   • ASK: "Which Sentinel workspace should I use for this analysis?"
   • ⛔ STOP AND WAIT for user response
   • ⛔ DO NOT proceed until user explicitly selects
4. If a query fails on the selected workspace:
   • ⛔ DO NOT automatically try another workspace
   • STOP and report the error, display available workspaces, ASK user to select

🔴 PROHIBITED ACTIONS:

• ❌ Selecting a workspace without user consent when multiple exist
• ❌ Switching to another workspace after a failure without asking
• ❌ Proceeding with analysis if workspace selection is ambiguous

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Output Modes

This skill supports two output modes. ASK the user which they prefer if not explicitly specified. Both may be selected.

Mode 1: Inline Chat Summary (Default)

• Render the full MCP usage analysis directly in the chat response
• Includes ASCII tables, trend charts, endpoint breakdowns, and security assessment
• Best for quick review and interactive follow-up questions

Mode 2: Markdown File Report

• Save a comprehensive report to reports/mcp-usage/MCP_Usage_Report_<timestamp>.md
• All ASCII visualizations render correctly inside markdown code fences (```)
• Includes all data from inline mode plus additional detail sections
• Use create_file tool — NEVER use terminal commands for file output
• Filename pattern: reports/mcp-usage/MCP_Usage_Report_YYYYMMDD_HHMMSS.md

Markdown Rendering Notes

• ✅ ASCII tables, box-drawing characters, and bar charts render perfectly in markdown code blocks
• ✅ Unicode block characters (▓░█) display correctly in monospaced fonts
• ✅ Emoji indicators (🔴🟢🟡⚠️✅) render natively in GitHub-flavored markdown
• ✅ Standard markdown tables (| col |) render as formatted tables
• Tip: Wrap all ASCII art in triple-backtick code fences for consistent rendering

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Scalability & Token Management

This skill was developed in a small lab environment (1–2 users, single workspace). In larger tenants with many users, MCP servers, and higher query volumes, the query complexity is not a concern — all queries use summarize, dcount, make_set(..., N), and take operators, so result sets remain bounded regardless of raw table size. Execution time will increase but output shape stays the same.

The primary risk in large environments is LLM token exhaustion during report generation. All query results accumulate in conversation context before the report is written, and this skill file itself consumes significant context. In a large tenant, richer result sets (more users, endpoints, error categories, AppIds) can push past token limits before the report is complete.

Guardrails for Large Environments

1. Tighten result set limits in queries:

Parameter	Small Env (default)	Large Env
make_set(..., N) for users	10	5
make_set(..., N) for endpoints	20–30	10
make_set(..., N) for errors	5	3
take on governance tables	25	15
take on endpoint rankings	25	15
take on error analysis	50	20

2. Incremental file writes (markdown mode):

Instead of composing the entire report in memory and writing it in one create_file call:

• Write the report header and executive summary first with create_file
• Append each section (Graph MCP, Sentinel Triage, Data Lake, etc.) using replace_string_in_file to insert content at the end of the file
• This allows earlier query results to fall out of active context after being written

3. Two-pass approach for very large tenants:

• Pass 1 (Summary): Run all queries with aggressive limits (take 10, make_set(..., 3)). Generate a summary report with top-level numbers only.
• Pass 2 (Drill-down): If the user wants detail on a specific section (e.g., "show me the full Data Lake error breakdown"), run targeted queries for that section only.

4. Parallel query batching:

Phases 1–5 contain independent queries — always run them in parallel. But avoid running all ~16 queries simultaneously; batch them into 2–3 groups of 5–6 queries. This balances throughput against context accumulation.

5. Omit raw query appendix for large reports:

The "Appendix: Query Details" section listing every KQL query used can be omitted in large environments to save tokens. The queries are documented in this skill file and don't need to be repeated in the report.

Indicators You're Hitting Token Limits

• Report generation starts but cuts off mid-section
• The agent switches to a new conversation turn unexpectedly during report writing
• Sections become progressively less detailed toward the end of the report
• The agent summarizes findings in chat instead of writing the full markdown file

If any of these occur, ask the agent to: "Continue writing the report from where you left off" — the incremental file write approach ensures partial progress is saved.

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Quick Start (TL;DR)

When a user requests MCP usage monitoring:

1. Select Workspace → list_sentinel_workspaces, auto-select or ask
2. Determine Output Mode → Ask if not specified: inline, markdown file, or both
3. Determine Time Range → Ask if not specified; default 30 days
4. Run Phase 1 (Graph MCP) → Daily usage summary, top endpoints, sensitive API access
5. Run Phase 2 (Sentinel Triage MCP) → API calls via AppId 7b7b3966, auth events, AH downstream queries
6. Run Phase 3 (Sentinel Data Lake MCP) → CloudAppEvents tool usage, error analysis, MCP vs Direct KQL
7. Run Phase 4 (Azure MCP & ARM) → ARM operations, resource provider breakdown
8. Run Phase 5 (Workspace Governance) → All query sources (Analytics + Data Lake tiers), MCP proportion
9. Run Phase 6 (Agent Identity) → Entra Agent ID detection, caller attribution
10. Run Phase 7 (Assessment) → Compute MCP Usage Score, security assessment, render report

Parallel execution: Phases 1-5 contain independent queries — run all of them in parallel for performance. Phases 6-7 depend on results from 1-5.

---

MCP Usage Score Formula

The MCP Usage Score is a composite health and risk indicator that summarizes MCP server activity. Unlike the Drift Score (which is a ratio), this is an absolute assessment based on multiple dimensions.

Scoring Dimensions

$$ \text{MCPUsageScore} = \sum_{i} \text{DimensionScore}_i $$

Each dimension contributes 0–20 points to a maximum of 100:

Dimension	Max Points	Green (0-5)	Yellow (6-12)	Red (13-20)
User Diversity	20	1-2 known users	3-5 users or 1 unknown	>5 users or unknown users
Endpoint Sensitivity	20	0% sensitive endpoints	1-30% sensitive	>30% calls to sensitive APIs
Error Rate	20	<1% errors	1-5% errors	>5% errors
Volume Anomaly	20	Within ±50% of daily avg	50-200% spike	>200% spike vs avg
Off-Hours Activity	20	<5% off-hours	5-20% off-hours	>20% calls outside business hours

Interpretation Scale

Score	Meaning	Action
0–25	Healthy	✅ Normal MCP usage, no concerns
26–50	Elevated	🟡 Review — minor anomalies detected
51–75	Concerning	🟠 Investigate — multiple risk signals present
76–100	Critical	🔴 Immediate review — significant security risk

Sensitivity Classification

Sensitive Graph API endpoints — flag any MCP calls to these patterns:

roleManagement, roleAssignments, roleEligibility,
authentication/methods, identityProtection, riskyUsers,
riskDetections, conditionalAccess, servicePrincipals,
appRoleAssignments, oauth2PermissionGrants,
auditLogs, directoryRoles, privilegedAccess,
security/alerts, security/incidents

Off-Hours Definition

Business hours: 08:00–18:00 local time (derive from user's primary sign-in timezone, or use UTC if unknown). Weekends count as off-hours for all 24 hours.

---

Execution Workflow

Phase 1: Graph MCP Server Analysis

Data source: MicrosoftGraphActivityLogs
Filter: AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"

Collect:

• Execute Query 1 to get daily usage summary with user count, success/error breakdown, avg duration
• Execute Query 2 to get top 25 Graph API endpoints accessed with call counts and last-used timestamps
• Execute Query 3 to get sensitive/high-risk endpoint access with user and scope details
• Execute Query 4 to discover all distinct AppIds calling Graph API for MCP identification

Phase 2: Sentinel Triage MCP Analysis

Data sources: MicrosoftGraphActivityLogs, SigninLogs, AADNonInteractiveUserSignInLogs
Filter: AppId = 7b7b3966-1961-47b5-b080-43ca5482e21c ("Microsoft Defender Mcp")

Detection Method (Confirmed Feb 2026):

The Sentinel Triage MCP has a dedicated AppId (7b7b3966-1961-47b5-b080-43ca5482e21c) that appears in both MicrosoftGraphActivityLogs and SigninLogs/AADNonInteractiveUserSignInLogs. This enables definitive attribution of Triage MCP calls — no heuristics or shared-surface estimation needed.

Key characteristics:

• AppDisplayName: "Microsoft Defender Mcp" (visible in SigninLogs)
• Auth type: Delegated + certificate (ClientAuthMethod=2) — user identity always available
• Scopes: SecurityAlert.Read.All, SecurityIncident.Read.All, ThreatHunting.Read.All
• Target resources: Microsoft Graph, WindowsDefenderATP
• API endpoints: POST /v1.0/security/runHuntingQuery/, GET /security/incidents/, GET /security/alerts_v2/
• No local SPN: Microsoft first-party app — display name only visible in SigninLogs, not in Graph API SPN lookup

🔵 MicrosoftGraphActivityLogs retention varies by environment (depends on Log Analytics workspace configuration and diagnostic settings). Do not assume a fixed retention period — check with a baseline row count query first.

Collect:

• Execute Query 5 to get authentication events by client app (VS Code, Copilot Studio, browser) with user, IP, OS, country
• Execute Query 6 to get client app usage breakdown with distinct user counts and last-seen timestamps
• Execute Query 7 to get Triage MCP API usage from MicrosoftGraphActivityLogs — filter by AppId 7b7b3966 for exact Triage MCP calls with endpoint/method/user breakdown
• Execute Query 7a to get Triage MCP authentication events from SigninLogs/AADNonInteractiveUserSignInLogs — sign-in frequency, user attribution, IP, OS, country
• Execute Query 7b to get LAQueryLogs for Advanced Hunting downstream queries via fc780465 / M365D_AdvancedHunting. Captures queries from any RunAdvancedHuntingQuery consumer (Triage MCP, Defender portal, Security Copilot) that hit connected LA tables. XDR-native tables (DeviceEvents, EmailEvents) don't appear here.
• Execute Query 7c to get portal/platform query volume from LAQueryLogs for governance context

Phase 3: Sentinel Data Lake MCP Analysis

Data source: CloudAppEvents (Purview unified audit log)
Execution tool: mcp_sentinel-data_query_lake — queries use TimeGenerated (Data Lake column). CloudAppEvents is available on both Data Lake (90d retention) and Advanced Hunting (30d, uses Timestamp). Always try Data Lake first for full retention coverage.
Filter: ActionType contains "Sentinel" or ActionType contains "KQL". RecordType is inside RawEventData (not a top-level column) — extract with parse_json(tostring(RawEventData)).RecordType. RecordType 403 = MCP tools, 379 = Direct KQL.

⚠️ MANDATORY: Execute Query 20 against query_lake before reporting any gap. If the query returns 0 results or table-not-found, THEN report the gap. Do NOT skip this phase based on assumptions about E5 licensing or Purview configuration — the table may be populated even without explicit Purview setup.

Audit Path: Sentinel Data Lake MCP tools are NOT audited via LAQueryLogs — they are tracked through Purview unified audit log, surfaced in the CloudAppEvents table. RecordType 403 (inside RawEventData) = Sentinel AI Tool activities, RecordType 379 = KQL activities.

MCP vs Direct KQL Delineation:

Access Pattern	RecordType	Interface	Operation	What It Represents
MCP Server-driven	403	IMcpToolTemplate	SentinelAIToolRunStarted, SentinelAIToolRunCompleted	Tool calls via Sentinel Data Lake MCP (e.g., query_lake, list_sentinel_workspaces, search_tables)
Direct KQL	379	Microsoft.SentinelGraph.AIPrimitives.Core.Services.KqsService	KQLQueryCompleted	KQL queries executed directly via Sentinel Graph / Data Lake Explorer (no MCP intermediary)

Key RawEventData Fields:

Field	Description	Example
ToolName	MCP tool invoked	query_lake, list_sentinel_workspaces, search_tables, analyze_url_entity
Interface	Execution interface — distinguishes MCP from direct	IMcpToolTemplate (MCP) vs KqsService (direct)
ExecutionDuration	Duration in seconds (as string)	"2.4731712"
FailureReason	Error message if failed	"SemanticError: 'DeviceDetail' column does not exist"
TablesRead	Tables accessed by the query	"SigninLogs"
DatabasesRead	Log Analytics workspace name	"la-yourworkspace"
TotalRows	Rows returned	100
InputParameters	Full tool input including KQL query text and workspaceId	JSON string with query and workspaceId keys

Collect:

• Execute Query 20 to get Data Lake MCP tool usage summary with success/failure breakdown
• Execute Query 21 to get tool-level breakdown with call counts and avg execution duration
• Execute Query 22 to get error analysis for failed Data Lake MCP tool calls
• Execute Query 23 to get daily activity trend across MCP and Direct KQL
• Execute Query 24 to get MCP vs Direct KQL delineation summary

Phase 4: Azure MCP Server & ARM Operations

Data sources: AzureActivity, SigninLogs, AADNonInteractiveUserSignInLogs, LAQueryLogs
Filter: Caller = user UPN (AzureActivity), AppId = 04b07795-8ddb-461a-bbee-02f9e1bf7b46 (sign-in logs, LAQueryLogs)

Collect:

• Execute Query 8 to get ARM operations by hour/IP with operation counts and resource provider sets
• Execute Query 9 to get resource provider breakdown with top operations per provider
• Execute Query 25 to get Azure MCP Server authentication events from SigninLogs/AADNonInteractiveUserSignInLogs — filter by AppId 04b07795 (Azure CLI credential, field-tested Feb 2026). 🔄 Previously documented as AppId 1950a258 (AzurePowerShellCredential) — that path is obsolete.
• Execute Query 26 to get Azure MCP Server workspace queries from LAQueryLogs — filter by AADClientId 04b07795. RequestClientApp is empty (not a unique fingerprint). Azure MCP appends \n| limit N to query text — use query text pattern as differentiator.

Detection Method (🔄 Updated Feb 2026):

The Azure MCP Server runs as a local .NET process (stdio mode) and authenticates via DefaultAzureCredential. Field-tested Feb 2026: The credential chain now resolves to Azure CLI credential (04b07795-8ddb-461a-bbee-02f9e1bf7b46), NOT AzurePowerShellCredential (1950a258) as previously documented.

Previous fingerprint (OBSOLETE): AppId 1950a258 + RequestClientApp = csharpsdk,LogAnalyticsPSClient. Only 1 occurrence found in 30-day lookback. The Azure MCP Server SDK path has changed.

Current fingerprint (field-tested Feb 2026):

Signal	Azure MCP Server (Current)	Azure CLI (Manual)	Notes
AppId (SigninLogs)	04b07795	04b07795	Shared — not a unique differentiator
AADClientId (LAQueryLogs)	04b07795	04b07795	Shared
RequestClientApp (LAQueryLogs)	Empty ("")	Empty ("")	Shared — not a unique differentiator. Empty RequestClientApp is also used by 4+ other AADClientIds
Query text pattern (LAQueryLogs)	Appends \n| limit N to all queries	No standard suffix	✅ Best differentiator — Azure MCP monitor_workspace_log_query always appends a limit operator
AzureActivity (Claims.appid)	04b07795 (write ops only)	04b07795	Shared; read ops not logged

🚨 Key change from previous documentation:

• ❌ RequestClientApp = "csharpsdk,LogAnalyticsPSClient" — OBSOLETE, no longer produced by Azure MCP Server
• ❌ AppId 1950a258 (AzurePowerShellCredential) — OBSOLETE credential path
• ✅ AppId 04b07795 (Azure CLI) — current credential path
• ✅ RequestClientApp is empty — shared with Azure CLI and other tools
• ✅ Query text containing \n| limit — most reliable query-level differentiator

Disambiguation challenges:

• Azure MCP Server queries are difficult to isolate from manual Azure CLI queries in LAQueryLogs because both share the same AppId AND empty RequestClientApp
• The \n| limit N suffix appended by monitor_workspace_log_query is the best heuristic but is not guaranteed to be unique
• In SigninLogs, UserAgent containing azsdk-net-Identity with OS Microsoft Windows may still help if the credential chain includes Azure Identity SDK components
• Consider correlating query timing with known MCP session activity for attribution

Authentication Sequence Observed (Current):

1. Azure MCP Server acquires token via Azure CLI cached credential
2. Token is reused for subsequent operations within its lifetime
3. If MFA claim is missing → interactive browser prompt (rare with CLI credential)
4. Subsequent calls reuse the cached token until expiry

🔴 Token Caching Behavior (Field-Tested Feb 2026):

• Sign-in events appear at token acquisition time, NOT at each individual API call time
• Once a token is cached, subsequent Azure MCP calls (list resources, get configs, etc.) do NOT generate new sign-in events
• You will see 1-3 sign-in events per token lifecycle, not one per API call
• To count actual API calls, correlate with AzureActivity (write ops) or LAQueryLogs (monitor_workspace_log_query calls)
• The ~1hr token lifetime means at most ~24 sign-in event clusters per day of continuous use

AzureActivity visibility: Only ARM write/action/delete operations appear in AzureActivity (Administrative category). Azure MCP Server read-only operations (list subscriptions, list resource groups, list clusters) do NOT appear. Claims.appid = 04b07795 when write operations do occur.

Note: Azure MCP Server is difficult to isolate from manual Azure CLI usage because they share the same AppId and both produce empty RequestClientApp. The \n| limit N query text suffix is the best heuristic for LAQueryLogs. In SigninLogs, the shared AppId means Azure MCP authenticated as Azure CLI — there is no unique sign-in fingerprint. Present findings as "Azure MCP Server / Azure CLI (shared AppId 04b07795)" in reports.

Phase 5: Workspace Query Governance

Data source: LAQueryLogs (Analytics tier), CloudAppEvents (Data Lake tier)
Filter: All AADClientIds (LAQueryLogs), All Sentinel operations (CloudAppEvents)

Collect:

• Execute Query 10 to get all clients querying the Analytics tier workspace with query counts, user counts, CPU usage
• Data Lake tier query volume from Phase 3 results (Queries 20-24)
• MCP proportion calculation: combined MCP query volume (Analytics + Data Lake tiers) / total query volume
• Execute Query 14 to get non-MCP platform context (Sentinel Engine, Logic Apps, Sentinel Portal volumes)

Phase 6: Agent Identity Detection

Data sources: MicrosoftGraphActivityLogs, AADServicePrincipalSignInLogs, AuditLogs, Microsoft Graph API

Collect:

• Execute Query 17 to get Graph MCP caller attribution — User vs SPN vs Agent breakdown
• Agent Identity inventory via Graph API — GET /servicePrincipals?$filter=servicePrincipalType eq 'Agent' (via Graph MCP microsoft_graph_suggest_queries → microsoft_graph_get)
• Execute Query 18 to get Agent Identity sign-in events from AADServicePrincipalSignInLogs where applicable
• Execute Query 19 to get Agent Identity CRUD operations from AuditLogs — creation, modification, deletion of agent identities

Note: This phase depends on Entra Agent ID (preview) being available in the tenant. If no agent identities exist, report: "✅ No Entra Agent Identities detected in tenant — all MCP callers are standard users or service principals." and skip Queries 18-19.

Phase 7: Score Computation & Report Generation

1. Compute per-dimension scores from Phase 1-6 data:
   • User Diversity: Count distinct users AND distinct Agent Identities across all MCP channels
   • Endpoint Sensitivity: % of Graph MCP calls to sensitive patterns (Phase 1 Query 3 vs Query 1)
   • Error Rate: % of non-2xx responses across all MCP channels
   • Volume Anomaly: Compare most recent day vs rolling average (Phase 1 Query 1 daily data)
   • Off-Hours Activity: % of MCP calls outside 08:00-18:00 (derive from TimeGenerated)
2. Sum dimension scores for composite MCP Usage Score
3. Include Agent Identity attribution in report if any agent callers detected (Phase 6)
4. Generate security assessment with emoji-coded findings
5. Render output in the user's selected mode

---

Sample KQL Queries

🔴 MANDATORY: Execute these queries EXACTLY as written. Substitute only the time range parameter (e.g., ago(30d) → ago(90d)) and entity-specific values where indicated. These queries are schema-verified and encode mitigations for pitfalls documented in Known Pitfalls. Rewriting, paraphrasing, or constructing "equivalent" queries from scratch risks hitting the exact schema issues these queries were designed to avoid.

Action	Status
Rewriting a pre-authored query from scratch	❌ PROHIBITED
Removing parse_json() / tostring() wrappers from queries	❌ PROHIBITED
Substituting column names without schema verification	❌ PROHIBITED
Using has instead of contains for CamelCase fields	❌ PROHIBITED
Executing a query not from this section without completing the Pre-Flight Checklist	❌ PROHIBITED

Query 1: Graph MCP — Daily Usage Summary

// Graph MCP daily usage trend with success/error breakdown
// Configurable: replace ago(30d) with desired lookback
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| summarize 
    Requests = count(),
    DistinctUsers = dcount(UserId),
    SuccessCount = countif(ResponseStatusCode >= 200 and ResponseStatusCode < 300),
    ErrorCount = countif(ResponseStatusCode >= 400),
    AvgDurationMs = avg(DurationMs)
    by bin(TimeGenerated, 1d)
| extend ErrorRate = round(100.0 * ErrorCount / Requests, 1)
| order by TimeGenerated desc

Query 2: Graph MCP — Top Endpoints Accessed

// Most-called Graph API endpoints via MCP, with user and recency info
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| extend Endpoint = tostring(split(RequestUri, "?")[0])
| summarize 
    CallCount = count(),
    DistinctUsers = dcount(UserId),
    LastUsed = max(TimeGenerated),
    Methods = make_set(RequestMethod, 5)
    by Endpoint
| order by CallCount desc
| take 25

Query 3: Graph MCP — Sensitive API Access

// Flag MCP calls to high-risk Graph endpoints (PIM, credentials, Identity Protection)
let sensitive_patterns = dynamic([
    "roleManagement", "roleAssignments", "roleEligibility",
    "authentication/methods", "identityProtection", "riskyUsers",
    "riskDetections", "conditionalAccess", "servicePrincipals",
    "appRoleAssignments", "oauth2PermissionGrants",
    "auditLogs", "directoryRoles", "privilegedAccess",
    "security/alerts", "security/incidents"
]);
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| where RequestUri has_any (sensitive_patterns)
| project TimeGenerated, UserId, RequestMethod, RequestUri, 
    ResponseStatusCode, IPAddress, Scopes
| order by TimeGenerated desc

Query 4: All Graph API AppIds — Discovery

// Discover all apps calling Graph APIs — find unknown MCP servers or AI agents
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| summarize 
    RequestCount = count(),
    DistinctUsers = dcount(UserId),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    SampleEndpoints = make_set(tostring(split(RequestUri, "?")[0]), 3)
    by AppId
| order by RequestCount desc

Query 5: Sentinel MCP — Authentication Events

⚠️ Pitfall-aware: Uses parse_json(Status) and parse_json(DeviceDetail) wrappers — see SigninLogs Status Field Needs parse_json(). Direct dot-notation fails in Data Lake.

// Who is authenticating to Sentinel MCP (via VS Code, Copilot Studio, browser)
SigninLogs
| where TimeGenerated >= ago(30d)
| where ResourceDisplayName =~ "Sentinel Platform Services"
| project TimeGenerated, UserPrincipalName, AppDisplayName, AppId,
    ResourceDisplayName, IPAddress, 
    tostring(parse_json(Status).errorCode) as ErrorCode,
    ConditionalAccessStatus, AuthenticationRequirement, ClientAppUsed,
    tostring(parse_json(DeviceDetail).operatingSystem) as OS,
    tostring(parse_json(LocationDetails).countryOrRegion) as Country
| order by TimeGenerated desc

Query 6: Sentinel MCP — Client App Breakdown

// Which client apps (VS Code, Copilot Studio, browser) are accessing Sentinel MCP
SigninLogs
| where TimeGenerated >= ago(30d)
| where ResourceDisplayName =~ "Sentinel Platform Services"
| summarize 
    SignInCount = count(),
    DistinctUsers = dcount(UserPrincipalName),
    Users = make_set(UserPrincipalName, 10),
    LastSeen = max(TimeGenerated)
    by AppDisplayName, AppId, ClientAppUsed
| order by SignInCount desc

Query 7: Sentinel Triage MCP — API Call Activity (Dedicated AppId)

// Measure Sentinel Triage MCP API calls via its dedicated AppId in MicrosoftGraphActivityLogs.
// AppId 7b7b3966 = "Microsoft Defender Mcp" — the Triage MCP server's own identity.
// This gives DEFINITIVE attribution of Triage MCP calls — no shared-surface estimation needed.
//
// Confirmed Feb 2026: AppId 7b7b3966 appears in MicrosoftGraphActivityLogs with delegated
// auth (certificate), full UserId attribution, and scopes SecurityAlert.Read.All,
// SecurityIncident.Read.All, ThreatHunting.Read.All.
//
// Known API endpoints:
//   - POST /v1.0/security/runHuntingQuery/ (Advanced Hunting)
//   - GET  /security/incidents/ (ListIncidents, GetIncidentById)
//   - GET  /security/alerts_v2/ (ListAlerts, GetAlertById)
let triage_mcp_appid = "7b7b3966-1961-47b5-b080-43ca5482e21c";
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == triage_mcp_appid
| extend Endpoint = extract(@"/v\d\.\d/(.+?)(\?|$)", 1, RequestUri)
| summarize 
    Calls = count(),
    DistinctUsers = dcount(UserId),
    Users = make_set(UserId, 10),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by RequestMethod, Endpoint
| order by Calls desc
| take 25

Query 7a: Sentinel Triage MCP — Authentication Events (SigninLogs)

// Triage MCP authentication events from SigninLogs + AADNonInteractiveUserSignInLogs.
// AppId 7b7b3966 = "Microsoft Defender Mcp" — delegated auth with certificate.
// Uses parse_json() wrappers for DeviceDetail/LocationDetails (Data Lake string columns).
let triage_mcp_appid = "7b7b3966-1961-47b5-b080-43ca5482e21c";
let signinlogs_interactive = SigninLogs
| where TimeGenerated >= ago(30d)
| where AppId == triage_mcp_appid
| extend SignInType = "Interactive"
| project TimeGenerated, UserPrincipalName, AppDisplayName, AppId,
    ResourceDisplayName, IPAddress,
    ResultType = tostring(ResultType),
    ResultDescription = tostring(ResultDescription),
    SignInType,
    OS = tostring(parse_json(DeviceDetail).operatingSystem),
    Browser = tostring(parse_json(DeviceDetail).browser),
    Country = tostring(parse_json(LocationDetails).countryOrRegion),
    City = tostring(parse_json(LocationDetails).city);
let signinlogs_noninteractive = AADNonInteractiveUserSignInLogs
| where TimeGenerated >= ago(30d)
| where AppId == triage_mcp_appid
| extend SignInType = "NonInteractive"
| project TimeGenerated, UserPrincipalName, AppDisplayName, AppId,
    ResourceDisplayName, IPAddress,
    ResultType = tostring(ResultType),
    ResultDescription = tostring(ResultDescription),
    SignInType,
    OS = tostring(parse_json(DeviceDetail).operatingSystem),
    Browser = tostring(parse_json(DeviceDetail).browser),
    Country = tostring(parse_json(LocationDetails).countryOrRegion),
    City = tostring(parse_json(LocationDetails).city);
union signinlogs_interactive, signinlogs_noninteractive
| summarize
    SignIns = count(),
    DistinctUsers = dcount(UserPrincipalName),
    Users = make_set(UserPrincipalName, 10),
    IPs = make_set(IPAddress, 10),
    Countries = make_set(Country, 10),
    LastSeen = max(TimeGenerated)
    by AppDisplayName, SignInType, ResourceDisplayName
| order by SignIns desc

Query 7b: LAQueryLogs — Advanced Hunting Downstream Queries (Supplementary Signal)

// SUPPLEMENTARY detection: Advanced Hunting queries (from Triage MCP, Defender portal,
// Security Copilot, or any RunAdvancedHuntingQuery consumer) that hit connected
// Log Analytics workspace tables.
//
// AH downstream queries appear under fc780465 (Sentinel Engine) with
// RequestClientApp "M365D_AdvancedHunting" — full user attribution (AADEmail populated).
//
// This is a DOWNSTREAM signal — it only fires when RunAdvancedHuntingQuery targets
// Sentinel-connected LA tables (SigninLogs, AuditLogs, SecurityAlert, etc.).
// Queries hitting XDR-native tables (DeviceEvents, EmailEvents, etc.) stay in the
// Defender XDR backend and never appear here.
//
// Use alongside Query 7 (MicrosoftGraphActivityLogs) for complete Triage MCP coverage:
//   - Query 7 = PRIMARY: Triage MCP API calls filtered by dedicated AppId 7b7b3966
//   - Query 7b = SUPPLEMENTARY: downstream query execution when AH hits LA tables
//
// ATTRIBUTION LIMITATION: Cannot distinguish Triage MCP AH queries from Defender portal
// AH queries or Security Copilot AH queries — all appear as M365D_AdvancedHunting.
LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "fc780465-2017-40d4-a0c5-307022471b92" and RequestClientApp == "M365D_AdvancedHunting"
| summarize 
    QueryCount = count(),
    DistinctUsers = dcount(AADEmail),
    Users = make_set(AADEmail, 10),
    AvgCPUMs = avg(StatsCPUTimeMs),
    TotalRowsReturned = sum(ResponseRowCount),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by AADClientId, RequestClientApp
| order by QueryCount desc

Query 7c: LAQueryLogs — Portal & Platform Query Volume (Non-MCP Governance)

// Query volume for Portal/Platform AppIds in LAQueryLogs (governance context).
// NOTE: These are NOT MCP servers — included for workspace query governance baseline.
// 80ccca67 = M365 Security & Compliance Center (Sentinel Portal backend)
// 95a5d94c = Azure Portal AppInsightsPortalExtension (Usage dashboards)
let portal_appids = dynamic([
    "80ccca67-54bd-44ab-8625-4b79c4dc7775",  // M365 Security & Compliance Center (Sentinel Portal)
    "95a5d94c-a1a0-40eb-ac6d-48c5bdee96d5"   // Azure Portal — AppInsightsPortalExtension (Usage dashboards)
]);
LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId in (portal_appids)
| extend Category = "Portal/Platform"
| summarize 
    QueryCount = count(),
    DistinctUsers = dcount(AADEmail),
    AvgCPUMs = avg(StatsCPUTimeMs),
    AvgRowsPerQuery = avg(ResponseRowCount),
    TotalRowsReturned = sum(ResponseRowCount),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by AADClientId, Category
| order by QueryCount desc

Query 8: Azure MCP — ARM Operations

// Azure ARM operations (may include Azure MCP Server — no dedicated AppId)
// Substitute <UPN> with the user's UPN
AzureActivity
| where TimeGenerated >= ago(30d)
| where Caller =~ '<UPN>'
| where CategoryValue == "Administrative"
| summarize
    OperationCount = count(),
    DistinctOperations = dcount(OperationNameValue),
    ResourceProviders = make_set(ResourceProviderValue, 10)
    by bin(TimeGenerated, 1d), CallerIpAddress
| order by TimeGenerated desc

Query 9: Azure MCP — Resource Provider Breakdown

// Azure resource types being accessed — detect unexpected resource access
// Substitute <UPN> with the user's UPN
AzureActivity
| where TimeGenerated >= ago(30d)
| where Caller =~ '<UPN>'
| where CategoryValue == "Administrative"
| summarize
    CallCount = count(),
    Operations = make_set(OperationNameValue, 5),
    DistinctResources = dcount(ResourceGroup)
    by ResourceProviderValue
| order by CallCount desc

Query 10: All Workspace Query Sources — Complete Governance View

// Every client querying the workspace — MCP and non-MCP combined
LAQueryLogs
| where TimeGenerated >= ago(30d)
| summarize 
    QueryCount = count(),
    DistinctUsers = dcount(AADEmail),
    AvgCPUMs = avg(StatsCPUTimeMs),
    TotalRowsReturned = sum(ResponseRowCount)
    by AADClientId
| order by QueryCount desc

Query 11: Graph MCP — Off-Hours Activity Detection

// Identify MCP calls outside business hours (08:00-18:00 UTC)
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| extend HourOfDay = datetime_part("hour", TimeGenerated)
| extend DayOfWeek = dayofweek(TimeGenerated) / 1d
| extend IsOffHours = HourOfDay < 8 or HourOfDay >= 18 or DayOfWeek >= 5
| summarize
    TotalCalls = count(),
    OffHoursCalls = countif(IsOffHours),
    BusinessHoursCalls = countif(not(IsOffHours)),
    OffHoursUsers = make_set_if(UserId, IsOffHours, 10)
    by bin(TimeGenerated, 1d)
| extend OffHoursPct = round(100.0 * OffHoursCalls / TotalCalls, 1)
| order by TimeGenerated desc

Query 12: Graph MCP — New User Detection (Baseline Comparison)

// Detect users who started using Graph MCP in the recent window but weren't in baseline
let baselineStart = ago(60d);
let baselineEnd = ago(7d);
let recentStart = ago(7d);
let baseline_users = MicrosoftGraphActivityLogs
| where TimeGenerated between (baselineStart .. baselineEnd)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| distinct UserId;
MicrosoftGraphActivityLogs
| where TimeGenerated >= recentStart
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| where UserId !in (baseline_users)
| summarize
    CallCount = count(),
    FirstSeen = min(TimeGenerated),
    Endpoints = make_set(tostring(split(RequestUri, "?")[0]), 10),
    IPs = make_set(IPAddress, 5)
    by UserId
| order by CallCount desc

Query 13: Sentinel MCP — Query Content Audit

// Audit actual KQL queries executed by MCP servers and Portal/Platform apps (sample)
let mcp_server_appids = dynamic([
    "7b7b3966-1961-47b5-b080-43ca5482e21c"   // Sentinel Triage MCP ("Microsoft Defender Mcp")
]);
let portal_appids = dynamic([
    "80ccca67-54bd-44ab-8625-4b79c4dc7775",  // M365 Security & Compliance Center (Sentinel Portal)
    "95a5d94c-a1a0-40eb-ac6d-48c5bdee96d5"   // Azure Portal — AppInsightsPortalExtension (Usage dashboards)
]);
LAQueryLogs
| where TimeGenerated >= ago(7d)
| where AADClientId in (mcp_server_appids) or AADClientId in (portal_appids)
| extend Category = case(
    AADClientId in (mcp_server_appids), "MCP Server",
    "Portal/Platform")
| project TimeGenerated, AADEmail, AADClientId, Category,
    QueryTextTruncated = substring(QueryText, 0, 200),
    ResponseCode, ResponseRowCount, StatsCPUTimeMs
| order by TimeGenerated desc
| take 50

Query 14: Unified MCP + Non-MCP Dashboard

// Combined MCP vs non-MCP view for workspace governance (Analytics tier)
let mcp_graph = MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| summarize Count = count() by Source = "Graph MCP", Category = "MCP", bin(TimeGenerated, 1d);
let mcp_triage = MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "7b7b3966-1961-47b5-b080-43ca5482e21c"
| summarize Count = count() by Source = "Sentinel Triage MCP (Microsoft Defender Mcp)", Category = "MCP", bin(TimeGenerated, 1d);
let sentinel_portal = LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "80ccca67-54bd-44ab-8625-4b79c4dc7775"
| summarize Count = count() by Source = "Sentinel Portal", Category = "Portal", bin(TimeGenerated, 1d);
let portal_appinsights = LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "95a5d94c-a1a0-40eb-ac6d-48c5bdee96d5"
| summarize Count = count() by Source = "Azure Portal (AppInsightsPortalExtension)", Category = "Portal", bin(TimeGenerated, 1d);
let platform_logic_apps = LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "de8c33bb-995b-4d4a-9d04-8d8af5d59601"
| summarize Count = count() by Source = "Logic Apps Connector", Category = "Platform", bin(TimeGenerated, 1d);
let platform_sentinel_engine = LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "fc780465-2017-40d4-a0c5-307022471b92"
| summarize Count = count() by Source = "Sentinel Engine", Category = "Platform", bin(TimeGenerated, 1d);
union mcp_graph, mcp_triage, sentinel_portal, portal_appinsights, platform_logic_apps, platform_sentinel_engine
| summarize TotalQueries = sum(Count) by Source, Category, bin(TimeGenerated, 1d)
| order by TimeGenerated desc

Note: This query covers the Analytics tier (LAQueryLogs + MicrosoftGraphActivityLogs). For Data Lake tier query governance, combine with Query 24 results from CloudAppEvents.

Query 15: Sentinel Audit — Configuration Changes

// Sentinel workspace configuration changes (analytics rules, connectors, etc.)
SentinelAudit
| where TimeGenerated >= ago(30d)
| summarize 
    ChangeCount = count(),
    OperationTypes = make_set(OperationName)
    by SentinelResourceType
| order by ChangeCount desc

Query 16: Logic Apps — Non-MCP Query Monitoring

// Track automated queries from Logic Apps (standard connector, NOT MCP)
LAQueryLogs
| where TimeGenerated >= ago(30d)
| where AADClientId == "de8c33bb-995b-4d4a-9d04-8d8af5d59601"
| summarize
    QueryCount = count(),
    DistinctQueries = dcount(QueryText),
    AvgCPUMs = avg(StatsCPUTimeMs),
    TotalRows = sum(ResponseRowCount),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by AADEmail, RequestClientApp
| order by QueryCount desc

Query 17: Graph MCP — Caller Attribution (User vs SPN vs Agent)

// Attribute Graph MCP calls to User, Service Principal, or Agent Identity
// Key: UserId populated = delegated (user), ServicePrincipalId populated = app-only (SPN/Agent)
// ClientAuthMethod: 0 = public client (user), 1 = client secret (SPN), 2 = certificate (SPN)
MicrosoftGraphActivityLogs
| where TimeGenerated >= ago(30d)
| where AppId == "e8c77dc2-69b3-43f4-bc51-3213c9d915b4"
| extend CallerType = case(
    isnotempty(ServicePrincipalId) and isempty(UserId), "ServicePrincipal/Agent (App-Only)",
    isnotempty(UserId) and isnotempty(ServicePrincipalId), "Delegated (User+SPN/Agent OBO)",
    isnotempty(UserId) and isempty(ServicePrincipalId), "User (Delegated)",
    "Unknown")
| extend AuthMethod = case(
    ClientAuthMethod == 0, "Public Client",
    ClientAuthMethod == 1, "Client Secret",
    ClientAuthMethod == 2, "Client Certificate",
    "Unknown")
| summarize
    CallCount = count(),
    DistinctEndpoints = dcount(tostring(split(RequestUri, "?")[0])),
    SuccessRate = round(100.0 * countif(ResponseStatusCode >= 200 and ResponseStatusCode < 300) / count(), 1),
    SampleEndpoints = make_set(tostring(split(RequestUri, "?")[0]), 5),
    IPs = make_set(IPAddress, 5)
    by CallerType, AuthMethod, UserId, ServicePrincipalId
| order by CallCount desc

Post-processing: For any rows where CallerType = "ServicePrincipal/Agent (App-Only)", cross-reference the ServicePrincipalId with Entra via Graph API:

1. Primary method (most reliable): Query /beta/servicePrincipals/{id}?$select=id,appId,displayName,servicePrincipalType,tags — check tags array for agentic indicators:
   • AgenticApp — confirms this is an agent application
   • AIAgentBuilder — agent was created by an AI agent builder platform
   • AgentCreatedBy:CopilotStudio — specifically created by Copilot Studio
   • AgenticInstance — runtime instance of an agent
   • power-virtual-agents-* — Copilot Studio internal tracking tag
2. Fallback: Check servicePrincipalType — if it equals "Agent", it is a registered Agent Identity. Note: as of Feb 2026, Copilot Studio agents still show "Application" here despite being true agents.
3. Name-based filtering is UNRELIABLE — SPNs with "Agent" in display name may be standard app registrations (e.g., "Contoso Agent Tools" = GitCreatedApp).

Use microsoft_graph_suggest_queries → microsoft_graph_get for the Graph API calls. Query multiple SPNs in one call: /beta/servicePrincipals?$count=true&$filter=id in ('id1','id2')&$select=id,appId,displayName,servicePrincipalType,tags.

Query 18: Agent Identity Sign-In Events

// Agent Identity sign-ins — look for Copilot Studio agent SPNs
// Field-tested: Copilot Studio agents sign in to "Bot Framework" from Azure internal IPv6 (fd00:34f2:*)
// Also check for Graph, Sentinel resources in case agents expand scope
// Cross-reference ServicePrincipalId values from Query 19 AuditLogs results
// Substitute <AGENT_SPN_IDS> with confirmed agent SPN IDs (or remove filter for broad discovery)
AADServicePrincipalSignInLogs
| where TimeGenerated >= ago(30d)
// Uncomment the next line if you have confirmed agent SPN IDs from Query 19 / Graph API:
// | where ServicePrincipalId in (<AGENT_SPN_IDS>)
| where ResourceDisplayName has_any ("Graph", "Sentinel", "Microsoft Graph", "Bot Framework")
| summarize
    SignInCount = count(),
    SuccessCount = countif(ResultType == "0" or ResultType == 0),
    FailCount = countif(ResultType != "0" and ResultType != 0),
    DistinctResources = dcount(ResourceDisplayName),
    Resources = make_set(ResourceDisplayName, 10),
    IPs = make_set(IPAddress, 5),
    Locations = make_set(Location, 5),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by ServicePrincipalName, ServicePrincipalId, AppId
| order by SignInCount desc

Telemetry patterns for Copilot Studio agents (field-tested):

• Resource: Bot Framework (NOT Microsoft Graph — agents talk to Bot Framework runtime)
• IP addresses: Azure internal IPv6 (fd00:34f2:*) — these are Azure infrastructure IPs, not user IPs
• Sign-in volume: Low (single-digit sign-ins typical for idle agents)
• Correlation: Match ServicePrincipalId here with TargetId from Query 19 AuditLogs to confirm the same agent

Query 19: Agent Identity CRUD Operations in AuditLogs

// Track creation, modification, and deletion of Agent Identities
// These operations indicate agent lifecycle management in the tenant
// Key insight: Copilot Studio agents are created by "Power Virtual Agents Service"
// and have display names like "Agent (Microsoft Copilot Studio)" or "<Name> (Microsoft Copilot Studio)"
AuditLogs
| where TimeGenerated >= ago(30d)
| where OperationName has_any ("agent", "Agent")
    or Category == "AgentIdentity"
    or (OperationName has_any ("service principal") and (
        tostring(TargetResources) has "agent"
        or tostring(TargetResources) has "Copilot Studio"
        or tostring(InitiatedBy) has "Power Virtual Agents"
    ))
| project TimeGenerated, OperationName, Result,
    InitiatedBy = coalesce(
        tostring(parse_json(tostring(InitiatedBy)).user.userPrincipalName),
        tostring(parse_json(tostring(InitiatedBy)).app.displayName)),
    TargetName = tostring(parse_json(tostring(parse_json(tostring(TargetResources))[0])).displayName),
    TargetId = tostring(parse_json(tostring(parse_json(tostring(TargetResources))[0])).id),
    TargetType = tostring(parse_json(tostring(parse_json(tostring(TargetResources))[0])).type)
| extend IsAgenticSPN = iff(
    InitiatedBy == "Power Virtual Agents Service"
    or TargetName has "Copilot Studio"
    or TargetName has "AgenticApp", true, false)
| order by TimeGenerated desc
| take 50

Interpreting results:

• InitiatedBy = Power Virtual Agents Service → Copilot Studio created/modified this agent SPN automatically
• InitiatedBy = [e-mail] → Human manually created/modified this SPN (may or may not be an agent)
• OperationName = Hard delete service principal shortly after Add service principal → Testing/experimentation pattern
• Multiple operations (Add SPN → Add owner → Update SPN) within seconds → Copilot Studio automated provisioning sequence

Query 20: Data Lake MCP — Tool Usage Summary

⚠️ Pitfall-aware: Uses contains (not has) for ActionType/Operation — see CloudAppEvents CamelCase Matching. Uses parse_json(tostring(RawEventData)) — see CloudAppEvents RawEventData Parsing. Filters on SentinelAIToolRunCompleted only — see CloudAppEvents Double-Counting Prevention.

// Sentinel Data Lake MCP tool usage from CloudAppEvents (Purview unified audit)
// RecordType 403 = Sentinel AI Tool activities (MCP), RecordType 379 = KQL activities (direct)
// IMPORTANT: Use 'contains' (not 'has') for ActionType — CamelCase values have no word boundaries
CloudAppEvents
| where TimeGenerated >= ago(30d)
| where ActionType contains "Sentinel" or ActionType contains "KQL"
| extend RawData = parse_json(tostring(RawEventData))
| extend 
    Operation = tostring(RawData.Operation),
    RecordType = toint(RawData.RecordType),
    ToolName = tostring(RawData.ToolName),
    Interface = tostring(RawData.Interface),
    ExecutionDuration = todouble(RawData.ExecutionDuration),
    FailureReason = tostring(RawData.FailureReason),
    TablesRead = tostring(RawData.TablesRead),
    DatabasesRead = tostring(RawData.DatabasesRead),
    TotalRows = toint(RawData.TotalRows),
    UserId_raw = tostring(RawData.UserId)
| extend 
    AccessPattern = case(
        RecordType == 403 and Interface == "IMcpToolTemplate", "MCP Server-Driven",
        RecordType == 379, "Direct KQL",
        "Other"),
    IsSuccess = isempty(FailureReason) or FailureReason == ""
| where Operation contains "Completed" or RecordType == 379  // Focus on completed events; use 'contains' not 'has' — CamelCase has no word boundaries
| summarize
    TotalCalls = count(),
    SuccessCount = countif(IsSuccess),
    FailureCount = countif(not(IsSuccess)),
    DistinctTools = dcount(ToolName),
    DistinctTables = dcount(TablesRead),
    AvgDurationSec = round(avg(ExecutionDuration), 2),
    TotalRowsReturned = sum(TotalRows),
    DistinctUsers = dcount(UserId_raw),
    Users = make_set(UserId_raw, 10)
    by AccessPattern
| extend ErrorRate = round(100.0 * FailureCount / TotalCalls, 1)
| order by TotalCalls desc

Query 21: Data Lake MCP — Tool Breakdown

⚠️ Pitfall-aware: Uses contains/parse_json(tostring()) pattern — see Query 20 pitfall notes. Uses todouble(ExecutionDuration) — see Data Lake MCP ExecutionDuration Format.

// Breakdown of individual MCP tool usage from CloudAppEvents
// Shows which Data Lake MCP tools are being called most frequently
CloudAppEvents
| where TimeGenerated >= ago(30d)
| where ActionType contains "SentinelAITool"
| extend RawData = parse_json(tostring(RawEventData))
| extend 
    Operation = tostring(RawData.Operation),
    ToolName = tostring(RawData.ToolName),
    ExecutionDuration = todouble(RawData.ExecutionDuration),
    FailureReason = tostring(RawData.FailureReason),
    TablesRead = tostring(RawData.TablesRead)
| where Operation == "SentinelAIToolRunCompleted"  // Only count completions to avoid double-counting
| extend IsSuccess = isempty(FailureReason) or FailureReason == ""
| summarize
    CallCount = count(),
    SuccessCount = countif(IsSuccess),
    FailureCount = countif(not(IsSuccess)),
    AvgDurationSec = round(avg(ExecutionDuration), 2),
    MaxDurationSec = round(max(ExecutionDuration), 2),
    TablesAccessed = make_set(TablesRead, 20),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by ToolName
| extend ErrorRate = round(100.0 * FailureCount / CallCount, 1)
| order by CallCount desc

Query 22: Data Lake MCP — Error Analysis

⚠️ Pitfall-aware: Uses contains/parse_json(tostring()) pattern — see Query 20 pitfall notes.

// Analyze failed Data Lake MCP tool calls — identify schema errors, permission issues, etc.
CloudAppEvents
| where TimeGenerated >= ago(30d)
| where ActionType contains "SentinelAITool"
| extend RawData = parse_json(tostring(RawEventData))
| extend 
    Operation = tostring(RawData.Operation),
    ToolName = tostring(RawData.ToolName),
    FailureReason = tostring(RawData.FailureReason),
    TablesRead = tostring(RawData.TablesRead),
    UserId_raw = tostring(RawData.UserId)
| where Operation == "SentinelAIToolRunCompleted"
| where isnotempty(FailureReason) and FailureReason != ""
| extend ErrorCategory = case(
    FailureReason has "SemanticError", "Schema/Semantic Error",
    FailureReason has "SyntaxError", "KQL Syntax Error",
    FailureReason has "Unauthorized" or FailureReason has "403", "Permission Denied",
    FailureReason has "Timeout", "Query Timeout",
    FailureReason has "NotFound", "Table/Resource Not Found",
    "Other Error")
| summarize
    ErrorCount = count(),
    Tools = make_set(ToolName, 10),
    Tables = make_set(TablesRead, 10),
    Users = make_set(UserId_raw, 10),
    SampleErrors = make_set(substring(FailureReason, 0, 150), 5),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated)
    by ErrorCategory
| order by ErrorCount desc

Query 23: Data Lake MCP — Daily Activity Trend