How do I find my Matomo token_auth?

Log in to Matomo, go to Personal > Security, and find the 'Auth tokens' section to generate or copy your token.

Can I track multiple sites?

Yes, but this integration currently requires a specific Site ID. You can list all sites using the `list_sites` tool to find the correct ID.

Is my analytics data secure?

Absolutely. Your token_auth is encrypted at rest and injected securely at runtime.

Matomo MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Matomo
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Matomo through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Matomo "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Matomo?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

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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 Matomo MCP Server

Connect your Matomo analytics instance to any AI agent and gain deep insights into your website traffic through natural conversation.

Pydantic AI validates every Matomo tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Visits Summary — Get aggregated metrics on visits, actions, and bounce rates
Real-time Monitoring — See the latest visitor details and actions as they happen
Top Content — Identify your most visited pages, referring websites, and social networks
Visitor Profiles — Inspect complete history and behavior for specific visitor IDs
Goal Tracking — List and monitor conversion goals configured in your instance

The Matomo MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Matomo to Pydantic AI via MCP

Follow these steps to integrate the Matomo MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Matomo with type-safe schemas

Why Use Pydantic AI with the Matomo MCP Server

Pydantic AI provides unique advantages when paired with Matomo through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Matomo integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Matomo connection logic from agent behavior for testable, maintainable code

Matomo + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Matomo MCP Server delivers measurable value.

Type-safe data pipelines: query Matomo with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Matomo tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Matomo and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Matomo responses and write comprehensive agent tests

Matomo MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Matomo to Pydantic AI via MCP:

get_goals

Get the list of goals

get_live_last_visits

Get last visits in real-time

get_site_details

Get details for a specific website

get_top_pages

Get the most visited pages

get_top_referrers

Get the top referrer types

get_top_socials

Get the top referring social networks

get_top_websites

Get the top referring websites

get_visitor_profile

Get a detailed profile for a visitor

get_visits_summary

Get a summary of visits

list_sites

List all websites in Matomo

Example Prompts for Matomo in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Matomo immediately.

"Show me a summary of visits for today."

"What are the top pages on my site this week?"

"List all sites configured in Matomo."

Troubleshooting Matomo MCP Server with Pydantic AI

Common issues when connecting Matomo to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Matomo + Pydantic AI FAQ

Common questions about integrating Matomo MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Matomo MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.