varunidealabs/mcp-server-deatils
If you are the rightful owner of mcp-server-deatils and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
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" } } } } }