Jinni
Jinni is an efficient tool for providing project context to large language models. It overcomes the limitations of reading files one by one by integrating relevant files and their metadata.
rating : 3.5 points
downloads : 247
What is Jinni?
Jinni is an intelligent project context extraction tool designed specifically for AI programming assistants. It can automatically scan your code project, extract all relevant file contents, and provide them to large language models (LLMs) in a structured manner, enabling AI to have a deeper understanding of the overall picture of your project.How to use Jinni?
Simply configure your AI development environment (such as Cursor/Roo/Claude, etc.), and Jinni will work in the background. When the AI needs to understand the project, it will automatically obtain the complete code context.Applicable scenarios
It is particularly suitable for scenarios such as the maintenance of complex code libraries, new members quickly getting familiar with the project, and cross - file code refactoring. It allows the AI assistant to understand your project like a senior developer.Main features
Intelligent context extractionAutomatically identify and extract source code files in the project, excluding irrelevant content such as binaries/logs
Seamless MCP integrationDeeply integrate with mainstream AI development tools through the Model Context Protocol
Custom filtering rulesSupport.gitignore - style rules to precisely control which files should be included or excluded
Complete metadataProvide metadata such as file path, size, and modification time to help understand file relationships
Seamless WSL supportAutomatically handle Windows/WSL path conversion, ensuring worry - free cross - platform development
Advantages and limitations
Advantages
Get a complete project view with one click without manually viewing each file
The intelligent filtering mechanism ensures that only relevant code files are included
Ready - to - use with mainstream AI programming tools
Support large - scale projects and automatically handle path and encoding issues
Limitations
For extremely large projects, you may need to adjust the inclusion rules to avoid overly long context
Simple development environment settings are required for the first - time configuration
Binary/non - text files are not included by default (can be modified through configuration)
How to use
Install Jinni
Install the Jinni package via pip or uv
Configure the MCP client
Add Jinni server settings to the configuration file of your AI development tool
Specify the project root directory (optional)
For security reasons, you can restrict Jinni to access only specific directories
Start using
Restart your development environment, and now the AI can request to read the project context
Usage examples
New members getting familiar with the projectNewly joined developers use AI to quickly understand the project structure and main modules
Cross - file refactoringEnsure consistency when refactoring code that affects multiple files
Dependency analysisSort out the module dependencies in a complex project
Frequently Asked Questions
Why can't the AI sometimes see some of my files?
How to limit the directory scope that Jinni can access?
How to handle Windows/WSL paths?
What should I do if the project is too large and the context exceeds the limit?
Related resources
Official GitHub repository
Get the latest version and source code
MCP protocol documentation
Understand the technical details of the Model Context Protocol
Cursor integration guide
How to configure Jinni in Cursor
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