How do I use Copilot Insights MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Copilot Insights MCP Server show me my top agents by usage this month" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Copilot Insights MCP Server

by syokiel

Overview Schema Related Servers Score Discussions

Python

Hybrid

Copilot Insights

Governance and telemetry reporting for Microsoft Copilot Studio agents across an M365 tenant. Pulls data from a dozen Azure and Microsoft 365 APIs, stores it in a local SQLite database, and outputs a multi-sheet Excel workbook — plus an MCP server that lets AI agents (Copilot Studio, M365 Copilot, AI Foundry) query the telemetry in plain English.

What it does

Collects usage, connector health, DLP violations, agent inventory, M365 Copilot adoption, and Copilot credit consumption from across your tenant
Imports CSV exports from the M365 Admin Center, Power Platform Admin Center, and Viva Insights
Stores everything in a local SQLite database (optionally synced to Azure Blob Storage)
Exports a timestamped Excel workbook with 30+ analytical sheets
Scores agent experience quality using an XLA model: Persona → Journey → XLA
Exposes an MCP server (stdio locally, HTTP on Azure Container Apps) so AI agents can query the data conversationally

Related MCP server: Fabric-Analytics-MCP

Data sources

Live API sources

Source	What it provides
Log Analytics / App Insights	Agent invocations, connector calls, OTel traces
Azure Monitor	Dependency failures, exceptions, alerts
Power Platform Admin API	Agent inventory, environments, DLP policies
Dataverse Web API	Agent solutions, publisher info
Microsoft Graph	M365 Copilot usage, Teams usage, user directory
Viva Insights	Person-level productivity signals
Microsoft Purview	Data governance signals
Microsoft Defender for Cloud	Security alerts, secure score

CSV imports (manual export → local file → auto-imported on sync)

Env var	Where to export from	What it populates
`VIVA_REPROT_CS_DIR`	Viva Insights / M365 Copilot Admin > Copilot Studio agents report > Export folder	Session metrics, topics, WAU, autonomous metrics
`VIVA_REPORT_ADOPTION`	Viva Insights > Copilot Adoption report > Export	Per-user weekly Copilot prompt counts by app
`VIVA_REPORT_IMPACT`	Viva Insights > Copilot Impact report > Export	Per-user work-pattern signals alongside Copilot activity
`M365ADMIN_AGENT_INVENTORY`	M365 Admin Center > Copilot > Agents > All agents > Export	Full agent registry with metadata, permissions, instructions
`M365ADMIN_USAGE_REPORT_AGENTS`	M365 Admin > Reports > Usage > M365 Copilot > Agents > Export (Agents tab)	30-day per-agent active users and responses
`M365ADMIN_USAGE_REPORT_AGENTUSERS`	M365 Admin > Reports > Usage > M365 Copilot > Agents > Export (Users & Agents tab)	30-day per-user per-agent activity
`M365ADMIN_USAGE_REPORT_USERS`	M365 Admin > Reports > Usage > M365 Copilot > Agents > Export (Users tab)	30-day per-user rollup (agents used, responses received)
`PPADMIN_LICENSES_CS_CONSUMPTION_ENV`	Power Platform Admin > Licensing > Copilot Studio > Export > Entitlement Consumption (Tenant)	Per-environment prepaid vs PAYG credit burn
`PPADMIN_LICENSES_CS_CONSUMPTION_AGENT`	Power Platform Admin > Licensing > Copilot Studio > Export > Entitlement Consumption (Per Agent)	Per-agent credit consumption by feature and channel
`PPADMIN_LICENSES_CS_CONSUMPTION_USER`	Power Platform Admin > Licensing > Copilot Studio > Export > Entitlement Consumption (Per User)	Per-user credit consumption
`PPADMIN_LICENSES_CS_CONSUMPTION_MANAGEAGENTS`	Power Platform Admin > Licensing > Copilot Studio > Manage Agents > Export	Daily capacity consumption by resource, feature, and channel
`AGENT_JOURNEY_MAP`	Maintained manually — see Experience Model below	Agent → Journey → Persona dimension for XLA scoring

Excel output sheets

Sheet	Contents
Summary	Tenant-wide KPI snapshot
KPI History	KPI trend over time
XLA_Measurements	XLA scorecard computed from session metrics
XLA_Persona_Journey	XLA scores aggregated by persona and journey (colour-coded)
XLA_Agent_Contribution	Per-agent breakdown of sessions, completion, escalation by persona/journey
Invocations	OTel conversation events
Connectors	Connector call detail with latency and success rates
AI_Model_Calls	Generative AI model call log
Agents	Full agent registry from Dataverse
Environments	Power Platform environments
Publishers	Dataverse publishers
DLP Policies	Data loss prevention policy list
M365_Copilot_Usage	Per-user M365 Copilot usage (Graph API)
M365_Copilot_Trend	Tenant-wide active user count trend
M365_Copilot_Packages	Copilot licence packages
M365_O365_Users	Broad O365 activity (Exchange, SharePoint, Teams)
M365_App_Users	Per-user M365 app activation status
M365_Agent_Inventory	Agent registry from M365 Admin Center
M365_Usage_Agents	30-day per-agent usage snapshot
M365_Usage_AgentUsers	30-day per-user per-agent activity
M365_Usage_Users	30-day per-user agent activity rollup
Teams_Usage	Teams chat, meeting, and call activity
Viva_Person_Insights	Person-level Viva productivity signals
Viva_CS_Sessions	Daily session outcomes and CSAT per agent
Viva_CS_Topics	Per-topic session breakdown
Viva_CS_WAU	Weekly active users per agent
Viva_CS_Autonomous	Daily autonomous run summary
Viva_Copilot_Adoption	Per-user weekly Copilot prompt counts by app
Viva_Copilot_Impact	Per-user productivity signals alongside Copilot activity
Tokenomics_Capacity	Daily capacity consumption by resource/feature/channel
Tokenomics_Entitlement	Per-environment prepaid vs PAYG entitlement burn
Tokenomics_PerAgent	Credit consumption broken down by agent
Tokenomics_PerUser	Credit consumption broken down by user
AzureMonitor_Health	Dependency failures and exceptions from Azure Monitor
CrossRef_Summary	Conversations with correlated OTel + Azure Monitor failures

Experience Model (XLA)

The XLA model shifts reporting from "did the agent work?" to "did the agent improve the experience for the right persona in the right journey?"

How it works

You maintain imports/agent_journey_persona_map.csv — a static file that assigns each agent to a journey and persona.
On every sync, this file is loaded into the dim_agent_journey_persona table.
Session metrics are joined against it to produce XLA scores per persona+journey combination.
Results appear in the XLA_Persona_Journey and XLA_Agent_Contribution sheets.

The mapping file

agent_id,agent_name,journey_name,persona_type
1b772240-...,IT Compass,Get Help,end_user
87393e81-...,Finance Policy Chat,Find Info,knowledge_worker
e2e9b9bf-...,Cyber Event Information Agent,Automate Work,operations

journey_name — one of four values:

Value	When to use
`Get Help`	User needs answers, support, or assistance
`Complete Task`	User needs to submit, create, or process something
`Find Info`	User needs to look something up or understand something
`Automate Work`	Agent runs autonomously with no direct user interaction

persona_type — who is using the agent:

Value	Who
`end_user`	General employee — broad audience
`it_support`	IT ops or service desk staff
`hr`	HR team or HR-driven processes
`knowledge_worker`	Analysts, finance, legal, sales — information-intensive roles
`operations`	Engineering or ops teams running processes or pipelines

Point AGENT_JOURNEY_MAP at the file in your .env. The agent_id must match exactly what appears in the Viva CS session reports (grab it from the Tokenomics or M365 Admin reports if needed). One agent can have multiple rows if it serves multiple journeys or personas.

XLA score formula: completion_rate × 0.6 + (100 − escalation_rate) × 0.2 + (100 − abandonment_rate) × 0.2

Scores ≥ 75 are green, 50–74 amber, < 50 red in the Excel sheet.

Prerequisites

Python 3.12+
Azure CLI (az login) — or a service principal with the permissions granted by provision/step2_identity.sh
An M365 tenant with Copilot Studio agents and Application Insights connected

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Configuration

Copy the example and fill in your values:

cp config/.env.example .env

Core variables

Variable	Description
`AZURE_TENANT_ID`	Entra ID tenant (required)
`AZURE_CLIENT_ID` / `AZURE_CLIENT_SECRET`	Service principal — omit to use `az login`
`LOG_ANALYTICS_WORKSPACE_ID`	App Insights workspace
`DATAVERSE_URL`	e.g. `https://orgXXXXXXXX.crm.dynamics.com`
`AZURE_STORAGE_ACCOUNT`	Blob storage account for the deployed MCP server
`LOOKBACK_DAYS`	How far back to pull data (default: 30)
`OUTPUT_PATH`	Excel output filename (default: `agent_telemetry.xlsx`)
`DB_PATH`	SQLite database path (default: `agent_telemetry.db`)

CSV import variables

# Viva / M365 Insights
VIVA_REPROT_CS_DIR=imports/June2026/CS+Agents+Report_YOKIEL
VIVA_REPORT_ADOPTION=imports/June2026/Copilot Adoption Report_YOKIEL.Csv
VIVA_REPORT_IMPACT=imports/June2026/Copilot Impact_YOKIEL.Csv

# M365 Admin Center
M365ADMIN_AGENT_INVENTORY=imports/June2026/Agents_2026-06-12_16_10_35.csv
M365ADMIN_USAGE_REPORT_AGENTS=imports/June2026/DeclarativeAgents_Agents_30_2026-06-12T16-09-57.csv
M365ADMIN_USAGE_REPORT_AGENTUSERS=imports/June2026/DeclarativeAgents_Users___agents_30_2026-06-12T16-09-29.csv
M365ADMIN_USAGE_REPORT_USERS=imports/June2026/DeclarativeAgents_Users_30_2026-06-18T18-11-03.csv

# Power Platform Admin Center — Copilot credit consumption (Tokenomics_* tables)
PPADMIN_LICENSES_CS_CONSUMPTION_ENV=imports/June2026/EntitlementConsumptionTenantDetailsReport_MCSMessages_180.csv
PPADMIN_LICENSES_CS_CONSUMPTION_AGENT=imports/June2026/EntitlementConsumptionTenantPerAgentDetailsReport_MCSMessages_180.csv
PPADMIN_LICENSES_CS_CONSUMPTION_USER=imports/June2026/EntitlementConsumptionTenantPerUserDetailsReport_MCSMessages_180.csv
PPADMIN_LICENSES_CS_CONSUMPTION_MANAGEAGENTS=imports/June2026/CapacityConsumptionTenantDetailsReport.csv

# Experience model — Agent → Journey → Persona mapping for XLA scoring
AGENT_JOURNEY_MAP=imports/agent_journey_persona_map.csv

For multi-tenant use, keep a separate .env file per tenant and pass it with --env:

python -m src.main --env .env.stryker all

Per-datasource auth overrides (e.g. LOG_ANALYTICS_CLIENT_ID) are documented in config/.env.example.

Usage

# Fetch all sources and export workbook in one step
python -m src.main all

# Fetch only (no export)
python -m src.main sync

# Export last sync to Excel (no new fetch)
python -m src.main export

# Import Copilot Studio analytics CSV exports from Viva
python -m src.main import-viva <path/to/csv/folder>

The workbook is written to OUTPUT_PATH (default: agent_telemetry_<timestamp>.xlsx).

CSV imports (Viva, M365 Admin, Power Platform, and the XLA mapping file) run automatically as part of sync and all whenever the corresponding env var is set.

MCP server

The MCP server lets AI agents (Copilot Studio, M365 Copilot, Azure AI Foundry) query telemetry in natural language.

Local (Claude Code / stdio)

Add to your .mcp.json:

{
  "mcpServers": {
    "agent-telemetry": {
      "command": "python",
      "args": ["-m", "src.mcp_server.server"]
    }
  }
}

Deployed (Azure Container Apps / HTTP)

bash deploy.sh deploy-config/<tenant>.env

The deploy script provisions an ACR, builds and pushes the Docker image, creates an Azure Container App, and wires up Entra ID authentication. Microsoft agent platforms send Bearer tokens automatically.

Provisioning a new tenant

Run the three provision scripts in order (from Azure Cloud Shell or with appropriate admin roles):

# 1. Create Log Analytics workspace + Application Insights
bash provision/step1_insights.sh

# 2. Grant the sync service principal all required read permissions + admin consent
bash provision/step2_identity.sh

# 3. Register the MCP server app in Entra ID
bash provision/step3_mcp.sh

See the header comments in each script for required admin roles and outputs.

Project structure

src/
  fetchers/       # One module per data source (API + CSV importers)
  writers/        # One module per Excel sheet
  mcp_server/     # MCP server (stdio + HTTP)
  agent/          # Bot Framework conversational agent
  store/          # SQLite + Azure Blob Storage
config/           # Settings and datasource config (gitignored — use .env)
imports/          # Drop CSV exports here (gitignored)
  agent_journey_persona_map.csv   # XLA experience model — agent → journey → persona
provision/        # One-time Azure setup scripts
deploy.sh         # Build + deploy to Azure Container Apps
Dockerfile        # Container image for the MCP server

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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