largefile

Largefile is an MCP server that helps you navigate, search, and edit very large files and codebases that exceed standard AI context windows. It exposes a set of specialized tools designed for working with massive files and directories, including semantic code navigation, pattern search, and safe edit workflows. You can inspect file structure and semantic outlines with get_overview, locate and count patterns with search_content, read specific sections or tail logs with read_content, and perform safe replacements with edit_content. If something goes wrong during edits, revert_edit provides a built-in recovery path. The server also supports directory-wide operations via list_directory and search_directory to explore large projects efficiently. To start, connect to the uvx-based MCP endpoint as shown in the Quick Start, then call the available tools to inspect and modify files without loading entire files into memory.

Prerequisites:

• Python 3.10+ installed on your system
• uv installed (https://docs.astral.sh/uv/getting-started/installation/)
• Access to the internet to install dependencies

Installation steps:

1. Install uvx (as shown in the Quick Start) and verify it is available in your PATH.

2. Set up the MCP server configuration. Create a configuration file (e.g., mcp_config.json) with the following content:

{
  "mcpServers": {
    "largefile": {
      "command": "uvx",
      "args": ["--from", "largefile", "largefile-mcp"]
    }
  }
}

3. Ensure the target largefile-mcp package or module is accessible via uvx (as per the project’s installation or runtime guide). If additional setup steps are required by your environment, follow those specifics from the uvx/largefile integration docs.

4. Start the MCP server using the configuration. The exact command may depend on your MCP runner, but it should align with the provided mcp_config example. For example:

# If your MCP runner consumes the config file directly
your-mcp-runner --config mcp_config.json

5. Verify the server is running and accessible at the expected endpoint, then begin issuing tool commands (get_overview, search_content, read_content, edit_content, revert_edit, list_directory, search_directory).

Notes and tips:

• The Largefile server is optimized for files larger than typical AI context windows; it uses memory strategies like RAM loading, memory-mapping, and streaming to handle multi-GB files.
• Tools support semantic analysis via Tree-sitter for supported languages (Python, JavaScript/JSX, TypeScript/TSX, Rust, Go, Java).
• Adjust environment variables under Configuration (LARGEFILE_MEMORY_THRESHOLD_MB, LARGEFILE_MMAP_THRESHOLD_MB, etc.) to tune performance for your hardware.
• For log files, use tail mode with read_content to efficiently inspect recent entries without loading the entire file.
• When performing edits across large files, always rely on backups created by edit_content and keep a quick revert path with revert_edit.
• If you encounter issues with large file handling, consider adjusting thresholds or enabling streaming modes as recommended in the Documentation.