How to integrate Google Analytics MCP with Pydantic AI

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Introduction

This guide walks you through connecting Google Analytics to Pydantic AI 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 Pydantic AI 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.

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Google Analytics
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Google Analytics workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed 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 starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Google Analytics
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Google Analytics
  • MCPServerStreamableHTTP connects to the Google Analytics MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Google Analytics
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["google_analytics"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Google Analytics tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
google_analytics_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[google_analytics_mcp],
    instructions=(
        "You are a Google Analytics assistant. Use Google Analytics tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Google Analytics endpoint
  • The agent uses GPT-5 to interpret user commands and perform Google Analytics operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Google Analytics.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Google Analytics API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Google Analytics
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["google_analytics"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    google_analytics_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[google_analytics_mcp],
        instructions=(
            "You are a Google Analytics assistant. Use Google Analytics tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Google Analytics.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Google Analytics through Composio's Tool Router. With this setup, your agent can perform real Google Analytics actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Google Analytics for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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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 Pydantic AI?

Yes, you can. Pydantic AI 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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