MCP Servers

Name: MCP Servers
Author: connectwithprakash

@connectwithprakash

代码仓库

关于 MCP Servers

Repository for my MCP servers

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

connectwithprakash

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "great-learning-mcp": {
      "command": "/path/to/python/virtualenv",
      "args": [
        "/path/to/mcp-servers/great_learning/great_learning_mcp.py"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Servers?

MCP Servers is a repository containing a collection of self-contained Model Context Protocol (MCP) implementations that enhance AI assistants with custom capabilities, such as accessing external tools, data sources, and proprietary information. It is designed for developers and users who want to extend AI assistants like Claude, Cursor, or Windsurf with specialized functions.

How to use MCP Servers?

Clone the repository, install dependencies with Poetry (Python 3.12+), and add the desired MCP’s configuration to your assistant’s MCP config file (e.g., ~/.cursor/mcp.json for Cursor). Provide the JSON with the path to a virtualenv and the MCP script’s arguments.

Key features of MCP Servers

Collection of self-contained MCP implementations for specific tasks
Great Learning MCP enables semantic search over course content
Detailed information retrieval for individual courses
Course comparison functionality
Full access to Great Learning documentation

Use cases of MCP Servers

AI assistant accessing up‑to‑date educational course information
Searching and comparing Great Learning programs
Integrating proprietary or private training data into assistant responses

FAQ from MCP Servers

What are the dependencies for using MCP Servers?

Python 3.12+, Poetry for dependency management, and any additional requirements specified in each MCP’s own README.

How do I configure an MCP server for my AI assistant?

Add the configuration to your assistant’s MCP configuration file (e.g., ~/.cursor/mcp.json for Cursor, ~/.claude.json for Claude Code) using the JSON format shown in the README with the correct command and args.