How to Use the Google Analytics MCP in LlamaIndex

Q: How does LlamaIndex index Google Analytics data from this MCP Server?

You use the McpToolSpec to pull the data via the MCP interface. Once retrieved, you pass the output to your LlamaIndex pipeline to be vectorized and stored.

Q: Can LlamaIndex query Google Analytics properties automatically?

Yes, by calling listproperties first, the agent discovers the correct IDs. It then uses those IDs to execute reports and index the results for your search application.

Q: How do I filter Google Analytics data before LlamaIndex indexing?

You provide filter expressions directly to the runreport tool. Only the data matching your criteria is returned, keeping your index clean and relevant.

Index your Google Analytics performance data directly into your LlamaIndex knowledge base for grounded, queryable insights.

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Connect Google Analytics MCP to LlamaIndex

Create your Vinkius account to connect Google Analytics to LlamaIndex 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 with LlamaIndex

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Convert Google Analytics reports into searchable context

Use `run_report` to generate performance snapshots and feed them into your LlamaIndex vector store. The agent treats the API output as a document, making your historical traffic data queryable by the LLM. You stop guessing about past performance. The agent finds specific trends by searching through indexed report results.

Discover metadata to build better indexes

Call `get_metadata` to map out all available metrics and dimensions for your property. LlamaIndex uses this to understand the schema before it starts pulling specific data points. It ensures the agent knows exactly which dimensions are available for filtering. You get more accurate data retrieval when the agent understands the underlying structure.

Track user journeys with detailed activity logs

Pull specific event histories using `get_user_activity` and index them for deep-dive analysis. Your LlamaIndex application can now perform RAG on specific user paths to identify bottlenecks. It turns raw event logs into an accessible knowledge base. You can ask the agent about specific user behaviors, and it finds the answer in the indexed activity data.

Setup guide

Set up Google Analytics MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Google Analytics MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Google Analytics tools.",
)
response = await agent.run("List recent Google Analytics data")

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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 MCP in LlamaIndex

You use the `McpToolSpec` to pull the data via the MCP interface. Once retrieved, you pass the output to your LlamaIndex pipeline to be vectorized and stored.

Yes, by calling `list_properties` first, the agent discovers the correct IDs. It then uses those IDs to execute reports and index the results for your search application.

You control the refresh cycle by triggering the tool calls again. The agent updates the indexed knowledge whenever you run a new report or pull current user activity.

You provide filter expressions directly to the `run_report` tool. Only the data matching your criteria is returned, keeping your index clean and relevant.

The server transmits reports directly to your local index. Your data remains within your controlled environment, and the MCP server only handles the bridge to the GA4 API.

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