What is pinmeto-location-mcp?

This MCP server integrates PinMeTo location data with AI assistants, enabling natural language queries to retrieve instant business insights from platforms like Google, Facebook, and Apple. It allows users to effortlessly monitor business performance, analyze customer reviews, and compare data across various locations and time periods.

pinmeto-location-mcp: This MCP server integrates PinMeTo location data with AI assistants,… (★ 15, MIT)

Features

01Centralized Location Management

02Multi-Platform Business Insights (Google, Facebook, Apple)

03Detailed Review and Rating Analysis

04Time Series Data Aggregation and Comparison

Why choose it

+Centralized Location Management

+Get average Google ratings across all business locations.

+Covers 5 supported environments or platforms, which is helpful for broader deployment needs.

+Ships with a public repository and a MIT license, which makes adoption and review easier.

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.

Compatibility

Claude Desktop

Client

Verified via docs

VS Code

Client

Verified via docs

Cursor

Client

Verified via docs

PinMeTo

Data Source

Verified via docs

Node.js/npm

Runtime

Verified via docs

Quick start

$ npx -y @pinmeto/pinmeto-location-mcp

Use cases

↳Get average Google ratings across all business locations.

↳Identify and respond to negative customer reviews effectively.

↳Compare current business performance metrics with previous periods or years.

↳Pinpoint top-performing locations based on various metrics.

How it compares

≈pinmeto-location-mcp sits in the API Integration category, so it makes more sense to evaluate it alongside tools like awesome-mcp-servers instead of in isolation.

≈If your main need is closer to "Get average Google ratings across all business locations.", that use case is a better lens for comparison than broad feature checklists alone.

≈pinmeto-location-mcp uses a MIT license, and community traction are both easier to judge in category context.