Pirsch Analytics MCP Server for Google ADKGive Google ADK instant access to 14 tools to Create Domain, Get Statistics Active, Get Statistics Events, and more

Name: Pirsch Analytics
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Pirsch Analytics as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

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The Pirsch Analytics MCP Server for Google ADK is a standout in the Data Analytics category — giving your AI agent 14 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="pirsch_analytics_agent",
    instruction=(
        "You help users interact with Pirsch Analytics "
        "using 14 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Pirsch Analytics MCP Server

Connect Pirsch Analytics to your AI agent to monitor your website traffic and user behavior without compromising privacy. This MCP server allows you to collect data and query complex statistics through natural language.

Google ADK natively supports Pirsch Analytics as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 14 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Traffic Monitoring — Retrieve overview, visitor, and page statistics for any of your domains using specific date ranges.
Event Tracking — Send individual or batch hits and events to track user conversions and interactions in real-time.
Domain Management — List all configured domains and create new ones directly through the API.
Referrer & Source Analysis — Analyze where your traffic is coming from with detailed referrer and UTM source statistics.
Real-time Insights — Access active visitor counts and goal completion metrics to stay on top of your site's performance.

Connect Pirsch Analytics to Google ADK via MCP

Follow these steps to wire Pirsch Analytics into Google ADK. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 14 tools from Pirsch Analytics via MCP

Why Use Google ADK with the Pirsch Analytics MCP Server

Google ADK provides unique advantages when paired with Pirsch Analytics through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Pirsch Analytics

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Pirsch Analytics tools with BigQuery, Vertex AI, and Cloud Functions

Pirsch Analytics + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Pirsch Analytics MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Pirsch Analytics and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Pirsch Analytics tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Pirsch Analytics regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Pirsch Analytics

Example Prompts for Pirsch Analytics in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Pirsch Analytics immediately.

"Get visitor statistics for domain ID 'abc-123' from 2023-10-01 to 2023-10-31."

"Track a page view for 'https://example.com/pricing' from IP 1.2.3.4."

"List all my domains configured in Pirsch."

Troubleshooting Pirsch Analytics MCP Server with Google ADK

Common issues when connecting Pirsch Analytics to Google ADK through Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Pirsch Analytics + Google ADK FAQ

Common questions about integrating Pirsch Analytics MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.