mcp-server-deatils

varunidealabs/mcp-server-deatils

3.1

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This document provides a structured overview of a Model Context Protocol (MCP) server setup, specifically for a local server configuration using a cloud configuration file on a Windows operating system.

The Model Context Protocol (MCP) server is designed to facilitate communication and data exchange between different components of a system, particularly in environments that utilize machine learning models and large language models (LLMs). The server acts as a bridge, ensuring that data is correctly formatted and transmitted between clients and services. In this setup, the server is configured locally on a Windows machine, utilizing a cloud configuration file to manage its settings. This allows for seamless integration with cloud-based services and APIs, such as Trello, by using specific API keys and tokens. The configuration is designed to be flexible, allowing for easy adjustments and scalability as needed.

Features

  • Local Server Configuration: Allows for easy setup and management of the MCP server on a Windows machine.
  • Cloud Integration: Utilizes a cloud configuration file to manage server settings and integrate with cloud-based services.
  • API Key Management: Supports secure handling of API keys and tokens for services like Trello.
  • Scalability: Designed to be easily scalable to accommodate growing data and service demands.
  • Flexibility: Offers a flexible setup that can be adjusted to meet specific user needs and requirements.

Usages

usage with claude

"trello-local": {
  "command": "uv",
  "args": [
    "run",
    "--directory",
    "C:\\Users\\varun\\Desktop\\MCP_testings\\mcp-trello",
    "mcp_trello\\server.py"
  ],
  "env": {
    "TRELLO_API_KEY": "APIKEY",
    "TRELLO_TOKEN": "KEY"
  }
}

usage with vscode

{
  "mcp": {
    "servers": {
      "trello-local": {
        "command": "uv",
        "args": [
          "run",
          "--directory",
          "C:\\Users\\varun\\Desktop\\MCP_testings\\mcp-trello",
          "mcp_trello\\server.py"
        ],
        "env": {
          "TRELLO_API_KEY": "APIKEY",
          "TRELLO_TOKEN": "KEY"
        }
      }
    }
  }
}