What does ProjectDocHelper do?
It generates a set of documentation files tailored to your project type (frontend, backend, or full stack) and serves them via MCP so that AI coding tools like Cursor can reference them for more accurate assistance.
@Stark1937
Basic information
ProjectDocHelper is an MCP (Model Context Protocol) server designed to automatically generate project documentation and make it accessible to AI development tools like Cursor through MCP, thereby improving the accuracy and relevance of AI responses.
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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ProjectDocHelper is an MCP (Model Context Protocol) server that automatically generates project documentation and makes it accessible to AI development tools like Cursor. It improves the accuracy and relevance of AI responses by providing structured project context.
Install with pip install projectdochelper. Generate documentation using projectdochelper generate --mode simple or --mode detailed, then start the MCP server with projectdochelper serve --port 8080. Configure the server address in Cursor to begin using the generated docs.
It generates a set of documentation files tailored to your project type (frontend, backend, or full stack) and serves them via MCP so that AI coding tools like Cursor can reference them for more accurate assistance.
Install with `pip install projectdochelper`. Then run `projectdochelper generate` to create docs and `projectdochelper serve` to start the MCP server (default port 8080).
Frontend projects generate ProjectRequirements.md, FrontendGuidelines.md, and TechStack.md. Backend projects generate ProjectRequirements.md, BackendStructure.md, and TechStack.md. Full stack projects also include all four documents.
Start the ProjectDocHelper server, then configure its MCP service address in Cursor. Once connected, Cursor can access the generated project documentation to enhance AI responses.
ProjectDocHelper automatically identifies relevant user-AI interactions and adds valuable information from those conversations into the corresponding documentation, keeping it structured and useful.

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