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Compresto MCP

by dqhieu

The Compresto MCP server provides AI assistants with real-time data about Compresto's usage statistics. It connects AI systems with external data sources, extending AI capabilities.

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What is Compresto MCP?

The Compresto MCP server is a Model Context Protocol server that provides AI assistants with real-time data about Compresto's usage statistics. It acts as a bridge between Compresto and AI systems, allowing them to access information about user activity, file processing, and size reduction.

How to use Compresto MCP?

To use the Compresto MCP server, you need to clone the repository, install the dependencies, build the project, and then configure your MCP settings file to point to the built index.js file. Once integrated, compatible AI assistants can access real-time data about Compresto's usage.

Key features of Compresto MCP

  • Provides real-time usage statistics

  • Offers tools to retrieve total users, processed files, and size reduction

  • Integrates with AI assistants via MCP

  • Easy to install and configure

  • Uses standard Node.js and npm

Use cases of Compresto MCP

  • Enhance AI assistant capabilities with Compresto usage data

  • Provide AI-driven insights into file compression trends

  • Automate reporting on Compresto's performance

  • Personalize user experiences based on usage patterns

  • Improve Compresto's features based on user behavior analysis

FAQ from Compresto MCP

What is Compresto?

Compresto is a file compression app that helps users reduce file sizes.

What is MCP?

The Model Context Protocol (MCP) is a standard that connects AI systems with external tools and data sources.

What data does this MCP server provide?

This MCP server provides data about total users, total processed files, and total size reduced by Compresto.

How do I install this MCP server?

Clone the repository, install dependencies using npm install, build the project using npm run build, and then configure your MCP settings file.

What are the prerequisites for development?

You need Node.js (v16 or higher) and npm or yarn.