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Access project files efficiently with filesystem operations that respect .gitignore.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "filesystem-gitignore" yet — see the docs or source repo.
Scan this project's source directories and read files related to the login feature, while automatically ignoring anything excluded by .gitignore.
Returns relevant non-ignored files or summaries for the login feature without reading unrelated build artifacts or dependency files.
List the main directories and key files in the current repository, following .gitignore to filter out files that do not need analysis.
Outputs a leaner repository view focused on the files that actually matter.
Find files related to API route implementations and summarize their roles without reading content ignored by .gitignore.
Provides a list of API-route-related files and brief explanations while reducing context spent on irrelevant files.
When developers ask AI to inspect a codebase, this tool can access filesystem content while automatically skipping files ignored by .gitignore. That helps focus on source code and configuration faster.
In large repositories, build outputs, caches, or dependency folders often distract analysis. By following .gitignore, this tool helps AI avoid those files, improving efficiency and reducing token usage.
It is an MCP server that provides filesystem operations while automatically following .gitignore rules. Its goal is to help Claude access project files more efficiently and with fewer tokens.
Its key difference is that it automatically respects .gitignore patterns. That reduces irrelevant files entering the analysis scope when reading a project.
The provided material does not include installation steps, runtime requirements, or key information. Please see the source repository for details.
Securely browse and search files in a read-only directory scope.
Batch-manage files, inspect project structure, and run commands for faster workflows.
Analyze project structure, read files, and understand codebases with context.
Securely perform cross-platform filesystem operations with encoding and path handling.
Enable AI to read, write, search, and manage files and folders.
Search files, content, and stats with natural language across codebases.