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by smat-dev • APIs & Integration
A tool to efficiently provide Large Language Models with project context.
Access consolidated project contexts for better analysis by Large Language Models
Efficiently filter and provide relevant project files while excluding unnecessary data
Integrate project context into AI tools using the Model Context Protocol
Jinni consolidates relevant project files into a single context view for Large Language Models, enabling efficient processing. It includes an MCP server for easy integration with AI tools and a CLI for manually handling project context. The tool intelligently filters project files using '.gitignore'-style patterns to exclude unnecessary content.
Retrieves the Jinni usage documentation (content of README.md).
Reads context from a specified project root directory (absolute path). Focuses on the specified target files/directories within that root. Returns a static view of files with paths relative to the project root. Assume the user wants to read in context for the whole project unless otherwise specified - do not ask the user for clarification if just asked to read context. If the user just says 'jinni', interpret that as read_context. If the user asks to list context, use the list_only argument. Both `targets` and `rules` accept a JSON array of strings. The `project_root`, `targets`, and `rules` arguments are mandatory. You can ignore the other arguments by default. IMPORTANT NOTE ON RULES: Ensure you understand the rule syntax (details available via the `usage` tool) before providing specific rules. Using `rules=[]` is recommended if unsure, as this uses sensible defaults. **Guidance for AI Model Usage** When requesting context using this tool: * **Default Behavior:** If you provide an empty `rules` list (`[]`), Jinni uses sensible default exclusions (like `.git`, `node_modules`, `__pycache__`, common binary types) combined with any project-specific `.contextfiles`. This usually provides the "canonical context" - files developers typically track in version control. Assume this is what the users wants if they just ask to read context. * **Targeting Specific Files:** If you have a list of specific files you need (e.g., `["src/main.py", "README.md"]`), provide them in the `targets` list. This is efficient and precise, quicker than reading one by one.
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