by smat-dev · MCP Server · ★ 270
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Jinni: Bring Your Project Into Context Jinni is a tool to efficiently provide Large Language Models the context of your projects. It gives a consolidated view of relevant project files, overcoming the limitations and inefficiencies of reading files one by one. Each file's content is preceded by a simple header indicating its path: path=src/app.py print("hello") The philosophy behind this tool is that LLM context windows are large, models are smart, and directly seeing your project best equips the model to help with anything you throw at it. There is an MCP (Model Context Protocol) server for integration with AI tools and a command-line utility (CLI) for manual use that copies project context to the clipboard ready to paste wherever you need it. These tools are opinionated about what counts as relevant project context to best work out of the box in most use cases, automatically excluding: Binary files Dotfiles and hidden directories Common naming conventions for logs, build directories, tempfiles, etc Inclusions/exclusions are customizable with complete granularity if required using – this works like except defining inclusions.
| Stars | 270 |
| Forks | 20 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.2329310282245/100 |
| Open Issues | 1 |
| Last Updated | 2025-12-14 |
| Created | 2025-04-02 |
| Platforms | mcp, python |
| Est. Tokens | ~129k |
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jinni is Bring your project into LLM context - tool and MCP server. It is categorized as a MCP Server with 270 GitHub stars.
jinni is primarily written in Python.
You can find installation instructions and usage details in the jinni GitHub repository at github.com/smat-dev/jinni. The project has 270 stars and 20 forks, indicating an active community.
jinni is released under the Apache-2.0 license, making it free to use and modify according to the license terms.