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mikusnuz/umami-mcp

by Various

Full-coverage MCP server for Umami Analytics API v2 — 66 tools, 2 resources, 2 prompts

M

MCP

mikusnuz/umami-mcp

Added 1 June 2026

#ai-tools #analytics #mcp #mcp-server #model-context-protocol #self-hosted #umami #web-analytics

Overview

A TypeScript MCP server that exposes 66 tools, 2 resources, and 2 prompts covering the Umami Analytics API v2. It allows builders to query and manage analytics data through the Model Context Protocol for integration with AI assistants and automation workflows.

Best for

Best for
Developers needing an MCP bridge to Umami Analytics for AI agent workflows

Use cases

  • Querying website traffic and event data from Umami via MCP-enabled agents
  • Automating analytics report generation with LLM tool calls
  • Integrating Umami dashboards into custom AI-powered analysis pipelines

Notes

A TypeScript MCP server that exposes 66 tools, 2 resources, and 2 prompts covering the Umami Analytics API v2. It allows builders to query and manage analytics data through the Model Context Protocol for integration with AI assistants and automation workflows.

1 stars on GitHub. Last updated 2026-03-19. Licensed MIT.

Use cases

  • Querying website traffic and event data from Umami via MCP-enabled agents
  • Automating analytics report generation with LLM tool calls
  • Integrating Umami dashboards into custom AI-powered analysis pipelines

Pros

  • Comprehensive coverage with 66 tools for the Umami API v2
  • Written in TypeScript for type safety and easy contribution
  • MCP protocol enables flexible use with many AI clients

Cons

  • Very low community adoption with only 1 star on GitHub
  • Potential instability or lack of maintenance due to limited interest
  • No documented examples or usage guides beyond the repository description

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Comprehensive coverage with 66 tools for the Umami API v2
  • Written in TypeScript for type safety and easy contribution
  • MCP protocol enables flexible use with many AI clients

Cons

  • Very low community adoption with only 1 star on GitHub
  • Potential instability or lack of maintenance due to limited interest
  • No documented examples or usage guides beyond the repository description