This research package contains comprehensive information on implementing Model Context Protocol (MCP) servers specifically for Cursor AI integration. The research focuses on how to create MCP servers that can be integrated with Cursor AI to enhance its capabilities through custom tools and services.
For a quick overview, start with the comprehensive_report.md
file, which contains all the research findings in a single document. For specific topics, refer to the individual files listed above.
To implement your own MCP server for Cursor AI, follow these steps:
1. Read the implementation_steps.md
file for a step-by-step guide
2. Review the code examples in the code_examples/
directory
3. Set up your development environment as described in the guide
4. Implement your custom tools based on the examples
5. Configure Cursor AI to use your MCP server
The code examples demonstrate different aspects of MCP server implementation:
1. Basic MCP Server (JavaScript): A simple server with text manipulation and calculation tools
2. Advanced MCP Server (TypeScript): A more sophisticated server with file system operations and security boundaries
3. Weather API Integration (TypeScript): An example of integrating with external APIs (OpenWeatherMap)
These examples can be used as starting points for your own MCP server implementations.
To run the code examples, you'll need:
- Node.js 14.x or higher
- npm or yarn package manager
- TypeScript (for TypeScript examples)
- Cursor AI with MCP support
This research package provides a comprehensive guide to implementing MCP servers for Cursor AI. By following the guidelines and examples, developers can create custom tools that enhance Cursor AI's capabilities for specific use cases.
I scraped a lot of information on MCP (Model Context Protocol) servers with integration to Cursor AI and Claude Desktop. That way you can add this folder to your preferred IDE so that it will have contextual indexing information available to grasp how to create MCP servers correctly.
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