Google Analytics MCP Server for AutoGen 12 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Google Analytics as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="google_analytics_agent",
tools=tools,
system_message=(
"You help users with Google Analytics. "
"12 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Google Analytics MCP Server
Connect your Google Analytics 4 (GA4) account to any AI agent and take full control of web and app analytics through natural conversation.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Google Analytics tools. Connect 12 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Custom Reports — Run reports with any combination of metrics (activeUsers, screenPageViews, sessions, eventCount) and dimensions (city, country, deviceCategory, channel grouping)
- Realtime Data — Monitor what's happening on your site right now with live user counts, events, and traffic sources from the last 30-60 minutes
- Batch Reports — Execute multiple report configurations in a single API call for efficient dashboard loading
- Metadata Discovery — List all available metrics and dimensions for your property, including custom definitions
- Compatibility Checks — Validate metric/dimension combinations before running reports to avoid errors
- Audience Exports — List and monitor audience export jobs for user segmentation and activation
- User Activity — Retrieve event history for specific users for journey analysis and support investigations
- Funnel Analysis — Visualize user progression through conversion steps and identify drop-off points
The Google Analytics MCP Server exposes 12 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Google Analytics to AutoGen via MCP
Follow these steps to integrate the Google Analytics MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 12 tools from Google Analytics automatically
Why Use AutoGen with the Google Analytics MCP Server
AutoGen provides unique advantages when paired with Google Analytics through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Google Analytics tools to solve complex tasks
Role-based architecture lets you assign Google Analytics tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Google Analytics tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Google Analytics tool responses in an isolated environment
Google Analytics + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Google Analytics MCP Server delivers measurable value.
Collaborative analysis: one agent queries Google Analytics while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Google Analytics, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Google Analytics data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Google Analytics responses in a sandboxed execution environment
Google Analytics MCP Tools for AutoGen (12)
These 12 tools become available when you connect Google Analytics to AutoGen via MCP:
batch_run_reports
Provide property_id and an array of report configurations. Each report can have different metrics, dimensions, and date ranges. This is efficient for dashboard loading or comparative analysis. The reports parameter should be a JSON array of report objects with metrics, dimensions, and dateRanges. Run multiple reports in a single API call
check_compatibility
Before running complex reports, use this to ensure compatibility between your chosen metrics and dimensions. This prevents errors and wasted API calls. Provide property_id and the metrics/dimensions you plan to use. Returns compatibility status and any conflicts that would prevent the report from running successfully. Check if metrics and dimensions can be combined in a report
get_audience_export
Audience exports allow you to extract user lists matching specific audience criteria. Use this to monitor the progress of audience extraction jobs. Provide property_id and the audience_export_id from list_audience_exports. Get status of a specific audience export
get_metadata
This includes both standard and custom metrics/dimensions with their descriptions, types, and compatibility information. Use this to discover what data is available before building reports. The propertyId is required and can be found in your GA4 admin settings. Get available metrics and dimensions for a GA4 property
get_property
Use the property_id obtained from list_properties to inspect property configuration. Get detailed information about a specific GA4 property
get_user_activity
This shows all interactions a user has had with your property, including pageviews, events, and conversions. Use this for user-level analysis, journey mapping, or support investigations. The userId must match the one sent with your tracking events. Get activity history for a specific user
list_accounts
This is the top-level container for properties. Each account can contain multiple properties. Use this to discover what accounts are available before drilling down into properties. List all Google Analytics accounts accessible to the user
list_audience_exports
Audience exports are used to extract user lists matching specific audience criteria for activation in other platforms. Shows status (CREATING, ACTIVE, FAILED) and configuration of each export job. List all audience export jobs for a property
list_properties
Properties represent individual websites, apps, or measurement streams. Each property has a unique ID needed for running reports. Use this to find the correct property_id for report queries. List all GA4 properties in an account
run_funnel_report
This helps identify where users drop off in conversion paths like checkout flows or signup processes. Provide property_id and a funnelSpec object defining the steps and breakdown settings. The funnelSpec should be a JSON object with steps array containing stepName, filterExpression, and optional breakdown settings. Run a funnel analysis report
run_realtime_report
Unlike standard reports, this shows what's happening on your site/app right now. Provide property_id and the metrics/dimensions you want to monitor in realtime. Common realtime metrics: activeUsers, eventCount, screenPageViews. Common realtime dimensions: city, country, deviceCategory, streamId. Get realtime analytics data (last 30-60 minutes)
run_report
You must provide the property_id, metrics (e.g., 'activeUsers', 'screenPageViews', 'eventCount'), and dimensions (e.g., 'city', 'pageTitle', 'sessionDefaultChannelGrouping'). Date ranges use YYYY-MM-DD format. Optional filter expression can narrow results. Common metrics: activeUsers, screenPageViews, sessions, eventCount, engagementRate, averageSessionDuration. Common dimensions: city, country, deviceCategory, sessionDefaultChannelGrouping, pageTitle, pagePath. Run a custom Google Analytics report
Example Prompts for Google Analytics in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Google Analytics immediately.
"Show me the number of active users and pageviews by country for the last 7 days for property 123456789."
"What's happening on the site right now? Show me realtime users by traffic source."
"Run a funnel analysis for our checkout flow: step 1 = viewed product, step 2 = added to cart, step 3 = started checkout, step 4 = completed purchase. Show me where users drop off."
Troubleshooting Google Analytics MCP Server with AutoGen
Common issues when connecting Google Analytics to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Google Analytics + AutoGen FAQ
Common questions about integrating Google Analytics MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Google Analytics with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Google Analytics to AutoGen
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.