How to integrate Google Analytics MCP with LlamaIndex

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Introduction

This guide walks you through connecting Google Analytics to LlamaIndex using the Composio tool router. By the end, you'll have a working Google Analytics agent that can show all google analytics accounts i manage, get detailed info for a specific account, list audiences for your ga4 property through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Google Analytics account through Composio's Google Analytics MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Google Analytics with

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Google Analytics
  • Connect LlamaIndex to the Google Analytics MCP server
  • Build a Google Analytics-powered agent using LlamaIndex
  • Interact with Google Analytics through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Google Analytics MCP server, and what's possible with it?

The Google Analytics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Analytics account. It provides structured and secure access to your analytics data, enabling your agent to analyze traffic, retrieve account info, list audiences, and build custom datasets on your behalf.

  • View and manage analytics accounts: Let your agent retrieve detailed information about specific Google Analytics accounts or list all accounts you have access to.
  • Audience insights and segmentation: Easily have your agent list all audiences associated with a GA4 property, helping you understand and segment your visitors.
  • Create custom expanded datasets: Direct your agent to combine key dimensions and metrics into tailored datasets for deeper analysis and reporting.
  • Efficient property and resource discovery: Have your agent confirm the existence of properties and fetch their details, streamlining your analytics management workflow.

Supported Tools & Triggers

Tools
Archive Custom DimensionTool to archive a CustomDimension on a property.
Batch Run Pivot ReportsTool to return multiple pivot reports in a batch for a GA4 property.
Batch Run ReportsTool to return multiple analytics data reports in a batch.
Check CompatibilityTool to list dimensions and metrics compatible with a GA4 report request.
Create Audience ExportTool to create an audience export for Google Analytics.
Create Audience ListTool to create an audience list for later retrieval by initiating a long-running asynchronous request.
Create Custom DimensionTool to create a CustomDimension for a Google Analytics property.
Create Custom MetricTool to create a custom metric in Google Analytics.
Create Expanded Data SetTool to create an expanded data set for a property.
Create Recurring Audience ListTool to create a recurring audience list that automatically generates new audience lists daily based on the latest data.
Create Report TaskTool to create a report task as a long-running asynchronous request for customized Google Analytics event data reports.
Create Rollup PropertyTool to create a roll-up property.
Get AccountTool to retrieve a single Account by its resource name.
Get Attribution SettingsTool to retrieve attribution configuration for a Google Analytics property.
Get AudienceTool to retrieve a single Audience configuration from a Google Analytics property.
Get Audience ExportTool to get configuration metadata about a specific audience export.
Get Audience ListTool to get configuration metadata about a specific audience list.
Get Custom DimensionTool to retrieve a single CustomDimension by its resource name.
Get Data Retention SettingsTool to retrieve data retention configuration for a Google Analytics property.
Get Data Sharing SettingsTool to retrieve data sharing configuration for a Google Analytics account.
Get Google Signals SettingsTool to retrieve Google Signals configuration settings for a GA4 property.
Get Key EventTool to retrieve a Key Event.
Get MetadataTool to get metadata for dimensions, metrics, and comparisons for a GA4 property.
Get PropertyTool to retrieve a single GA4 Property by its resource name.
Get Property Quotas SnapshotTool to retrieve all property quotas organized by category (corePropertyQuota, funnelPropertyQuota, realtimePropertyQuota) for a given GA4 property.
Get Recurring Audience ListTool to get configuration metadata about a specific recurring audience list.
Get Report TaskTool to get report metadata about a specific report task.
List Account SummariesTool to retrieve summaries of all Google Analytics accounts accessible by the caller.
List Accounts (v1beta)Tool to list all Google Analytics accounts accessible by the caller using v1beta API.
List AdSense LinksTool to list all AdSenseLinks on a property.
List Audience ExportsTool to list all audience exports for a property.
List Audience ListsTool to list all audience lists for a specified property to help find and reuse existing lists.
List AudiencesTool to list Audiences on a property.
List BigQuery LinksTool to list BigQuery Links on a property.
List Calculated MetricsList Calculated Metrics
List Channel GroupsTool to list ChannelGroups on a property.
List Conversion EventsTool to list conversion events on a property.
List Custom DimensionsList Custom Dimensions
List Custom MetricsTool to list CustomMetrics on a property.
List DataStreamsTool to list DataStreams on a property.
List Display & Video 360 Advertiser LinksTool to list Display & Video 360 advertiser links on a property.
List DisplayVideo360 Advertiser Link ProposalsTool to list DisplayVideo360AdvertiserLinkProposals on a property.
List Event Create RulesTool to list EventCreateRules configured on a web data stream.
List Expanded Data SetsTool to list ExpandedDataSets on a property.
List Firebase LinksTool to list FirebaseLinks on a property.
List Google Ads LinksTool to list GoogleAdsLinks on a property.
List Key EventsTool to list Key Events.
List Measurement Protocol SecretsTool to list MeasurementProtocolSecrets under a data stream.
List PropertyTool to list GA4 properties based on filter criteria.
List Recurring Audience ListsTool to list all recurring audience lists for a GA4 property.
List Reporting Data AnnotationsTool to list all Reporting Data Annotations for a specific property.
List Report TasksTool to list all report tasks for a Google Analytics property.
List Search Ads 360 LinksTool to list all SearchAds360Links on a property.
List SKAdNetwork Conversion Value SchemasTool to list SKAdNetworkConversionValueSchema configurations for an iOS data stream.
List Subproperty Event FiltersTool to list all subproperty event filters on a property.
List Subproperty Sync ConfigsTool to list SubpropertySyncConfig resources for managing subproperty synchronization configurations.
Provision Account TicketTool to request a ticket for creating a Google Analytics account.
Query Audience ExportTool to query a completed audience export.
Query Audience ListTool to query an audience list.
Query Report TaskTool to retrieve a report task's content.
Run Funnel ReportTool to run a GA4 funnel report.
Run Pivot ReportTool to run a customized pivot report of Google Analytics event data.
Run Realtime ReportTool to run a customized realtime report of Google Analytics event data.
Run ReportTool to run a customized GA4 data report.
Send EventsTool to send event data to Google Analytics 4 using the Measurement Protocol.
Update PropertyTool to update an existing GA4 Property.
Validate EventsTool to validate Measurement Protocol events before sending them to production.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Google Analytics account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Google Analytics

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Google Analytics access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called google analytics_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["google_analytics"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Google Analytics actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Google Analytics actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, google analytics)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Google Analytics tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Google Analytics database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Google Analytics

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Google Analytics, then start asking questions.

Complete Code

Here's the complete code to get you started with Google Analytics and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["google_analytics"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Google Analytics actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Google Analytics actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Google Analytics to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Google Analytics tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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FAQ

What are the differences in Tool Router MCP and Google Analytics MCP?

With a standalone Google Analytics MCP server, the agents and LLMs can only access a fixed set of Google Analytics tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Google Analytics and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Google Analytics tools.

Can I manage the permissions and scopes for Google Analytics while using Tool Router?

Yes, absolutely. You can configure which Google Analytics scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Google Analytics data and credentials are handled as safely as possible.

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