Google Analytics MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Google Analytics through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"google-analytics": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Google Analytics, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Google Analytics through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Google Analytics MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 12 tools from Google Analytics via MCP
Why Use LangChain with the Google Analytics MCP Server
LangChain provides unique advantages when paired with Google Analytics through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Google Analytics MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Google Analytics queries for multi-turn workflows
Google Analytics + LangChain Use Cases
Practical scenarios where LangChain combined with the Google Analytics MCP Server delivers measurable value.
RAG with live data: combine Google Analytics tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Google Analytics, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Google Analytics tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Google Analytics tool call, measure latency, and optimize your agent's performance
Google Analytics MCP Tools for LangChain (12)
These 12 tools become available when you connect Google Analytics to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Google Analytics to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersGoogle Analytics + LangChain FAQ
Common questions about integrating Google Analytics MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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Connect Google Analytics to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.