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amazon-fresh-server

by Alec2435

This is a TypeScript-based MCP server that implements a simple notes system. It demonstrates core MCP concepts by providing resources, tools, and prompts for interacting with notes.

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What is amazon-fresh-server?

The amazon-fresh-server is a Model Context Protocol (MCP) server built with TypeScript. It provides a simple notes system, allowing users to create, access, and summarize notes using MCP resources, tools, and prompts.

How to use amazon-fresh-server?

To use the server, install the dependencies, build the server, and then add the server configuration to your Claude Desktop configuration file. You can then interact with the server through Claude Desktop using the provided resources, tools, and prompts. Debugging is facilitated by the MCP Inspector.

Key features of amazon-fresh-server

  • Resources representing text notes with URIs and metadata

  • Tools for creating new notes with titles and content

  • Prompts for generating summaries of stored notes

  • Plain text mime type for simple content access

Use cases of amazon-fresh-server

  • Creating and managing personal notes within Claude Desktop

  • Summarizing notes for quick review and understanding

  • Demonstrating core MCP concepts and server implementation

  • Integrating with LLMs for note summarization

  • Building custom workflows around note management

FAQ from amazon-fresh-server

What is MCP?

MCP stands for Model Context Protocol, a protocol for communication between applications and language models.

What is the purpose of this server?

This server demonstrates MCP concepts by providing a simple notes system.

How do I install the server?

Install dependencies using npm install, build the server using npm run build, and add the server configuration to your Claude Desktop configuration file.

How do I debug the server?

Use the MCP Inspector by running npm run inspector.

What are the available tools?

The server provides a create_note tool for creating new notes and a summarize_notes prompt for generating summaries.