How to Use the Google Analytics 4 MCP in Pydantic AI

Q: How does Pydantic AI validate Google Analytics 4 data structures?

When your agent calls getpageviews or gettrafficsources, the raw JSON payload is parsed directly into a Pydantic model. If a metric like activeUsers is missing or returns an invalid type, Pydantic AI throws a validation error instantly.

Q: How do I initialize the Google Analytics 4 toolset in Pydantic AI?

Use the unified MCPToolset("http://...") helper pointing to your Vinkius endpoint, then pass it in the toolsets list of your Agent. Avoid the deprecated MCPServerHTTP class.

Q: What happens if Google Analytics 4 returns empty data?

If runreport returns an empty rows array, your Pydantic AI models should define that field as an optional list or set a default empty list. This ensures your agent handles low-traffic days gracefully without crashing.

Query GA4 with absolute type safety using Pydantic AI to validate dimensions and metrics at runtime.

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Connect Google Analytics 4 MCP to Pydantic AI

Create your Vinkius account to connect Google Analytics 4 to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Google Analytics 4 with Pydantic AI

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Type-safe conversion tracking with Pydantic AI

`get_conversions` fetches your critical event goals and validates the response schema against strict Pydantic models before your agent processes them. If the GA4 API returns an unexpected null value or a modified string format, Pydantic AI raises a validation error immediately. This prevents silent data corruption in your reporting pipelines. Your agent works with guaranteed types, ensuring that conversion counts are always integers and event names are always valid strings.

Parsing page views with strict schemas

`get_page_views` pulls top URLs and active visitor counts into a pre-defined schema using this MCP Server. The Pydantic AI agent parses the raw JSON response from the server, mapping the fields directly to typed Python objects. This structure makes it easy to feed analytics data into downstream databases or visualization libraries. You write clean, typed Python code without worrying about missing keys or malformed API responses.

Validating complex multi-dimensional reports

`run_pivot_report` executes complex multi-dimensional queries that can return highly nested JSON structures. Pydantic AI enforces a strict model on this output, ensuring your agent never hallucinates metrics or misinterprets row headers. If the pivot structure changes, the application fails loudly during development rather than corrupting your production dashboards. This strict validation makes the MCP server highly reliable for enterprise financial reporting.

Setup guide

Set up Google Analytics 4 MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "google-analytics-4-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Google Analytics 4 tools.",
)

result = await agent.run("List recent Google Analytics 4 transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google Analytics 4. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Google Analytics 4 MCP in Pydantic AI

When your agent calls `get_page_views` or `get_traffic_sources`, the raw JSON payload is parsed directly into a Pydantic model. If a metric like activeUsers is missing or returns an invalid type, Pydantic AI throws a validation error instantly.

Yes, Pydantic AI is model-agnostic. You can connect the GA4 MCP server to agents running local models, OpenAI, or Anthropic, ensuring type-safe tool execution across any LLM.

Use the unified `MCPToolset("http://...")` helper pointing to your Vinkius endpoint, then pass it in the `toolsets` list of your Agent. Avoid the deprecated MCPServerHTTP class.

If `run_report` returns an empty rows array, your Pydantic AI models should define that field as an optional list or set a default empty list. This ensures your agent handles low-traffic days gracefully without crashing.

The MCP server handles read-only queries for your traffic, device, and demographic data. All authentication tokens are isolated in Vinkius's zero-trust sandbox, and no user-identifiable data is stored on the server.

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