Umami MCP logo

Umami MCP

OrganizationPopular
umami-software

Umami is a privacy-first analytics platform. Traffic, campaigns, behavior, conversions, and revenue in one place — no cookies, no surveillance, self-hosted or in the cloud.

Publisherumami-software
Repositoryumami
LanguageTypeScript
Forks
8.1K
Stars
38.9K
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Umami MCP exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    38.9K stars and 8.1K forks from the linked repository.


🚀 Getting Started

A detailed getting started guide can be found at umami.is/docs.


🛠 Installing from Source

Requirements

  • A server with Node.js version 18.18+.
  • A PostgreSQL database version v12.14+.

Get the source code and install packages

bash
git clone https://github.com/umami-software/umami.git
cd umami
pnpm install

Configure Umami

Create an .env file with the following:

bash
DATABASE_URL=connection-url

Optional: set API_URL to change the base URL used by internal UI API calls. Relative paths are served under BASE_PATH; absolute URLs are proxied through the local /api route. For example, API_URL=/internal-api or API_URL=https://api.example.com/api.

Optional: set TWO_FACTOR_ENCRYPTION_KEY to a 64-character hex string to enable two-factor authentication. Generate one with openssl rand -hex 32. Two-factor authentication is unavailable and cannot be required until this key is set.

MCP is disabled by default. Set MCP_ENABLED=1 to enable the /mcp endpoint, then authenticate with an API key generated under Settings → API keys.

The connection URL format:

bash
postgresql://username:mypassword@localhost:5432/mydb

Build the Application

bash
pnpm run build

The build step will create tables in your database if you are installing for the first time. It will also create a login user with username admin and password umami.

Start the Application

bash
pnpm run start

By default, this will launch the application on http://localhost:3000. You will need to either proxy requests from your web server or change the port to serve the application directly.


🐳 Installing with Docker

Umami provides Docker images as well as a Docker compose file for easy deployment.

Docker image:

bash
docker pull docker.umami.is/umami-software/umami:latest

Docker compose (Runs Umami with a PostgreSQL database):

bash
docker compose up -d

🔄 Getting Updates

To get the latest features, simply do a pull, install any new dependencies, and rebuild:

bash
git pull
pnpm install
pnpm build

To update the Docker image, simply pull the new images and rebuild:

bash
docker compose pull
docker compose up --force-recreate -d

🛟 Support

Use Umami MCP MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Umami MCP is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Umami MCP as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Umami MCP MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "umami-software-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Umami MCP MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Umami MCP MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Umami MCP to help me with this task?
Umami MCP
Sure. I read it.
Here is what I found using Umami MCP.

Frequently asked questions

What is the Umami MCP MCP server used for?

Umami MCP is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Umami MCP MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Umami MCP with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Umami MCP MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Umami MCP connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Umami MCP MCP to TypingMind?

Umami MCP runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Umami MCP MCP provide in TypingMind?

Umami MCP exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Umami MCP MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Umami MCP requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

Related MCP Servers

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇