Matomo MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Matomo through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"matomo": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Matomo, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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.
LangChain's ecosystem of 500+ components combines seamlessly with Matomo through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Matomo MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Matomo via MCP
Why Use LangChain with the Matomo MCP Server
LangChain provides unique advantages when paired with Matomo through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Matomo MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Matomo queries for multi-turn workflows
Matomo + LangChain Use Cases
Practical scenarios where LangChain combined with the Matomo MCP Server delivers measurable value.
RAG with live data: combine Matomo tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Matomo, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Matomo tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Matomo tool call, measure latency, and optimize your agent's performance
Matomo MCP Tools for LangChain (10)
These 10 tools become available when you connect Matomo to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Matomo to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersMatomo + LangChain FAQ
Common questions about integrating Matomo MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Matomo with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Matomo to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.