What operations does the server support?
It supports six tools: `list_directory`, `read_file`, `save_file`, `append_file`, `delete_this_file`, and `edit_file` (which offers line-based pattern matching and dry-run previews).
@asirulnik
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp_server_filesystem_01": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Lists files and directories in the project directory
Reads the contents of a file
Creates or overwrites files atomically
Adds content to existing files
Removes files from the filesystem
Makes selective edits using pattern matching
MCP File System Server is a Model Context Protocol server that enables AI assistants (via Claude Desktop, VSCode/Cline extension, or other MCP-compatible systems) to perform file system operations—read, write, edit, delete—within a specified project directory. It is built for developers who want to collaborate with AI on local codebases through natural-language prompts.
Install the server by cloning the repository and running pip install -e . inside its directory. Then launch it with python -m src.main --project-dir /path/to/project, optionally setting --log-level and --log-file. Add the server’s configuration to your MCP client’s settings (e.g., Claude Desktop’s claude_desktop_config.json or Cline’s cline_mcp_settings.json) and restart the client.
list_directory: list files and directories in the project directoryread_file: read the contents of a filesave_file: atomically create or overwrite files with contentappend_file: append content to an existing filedelete_this_file: permanently delete a file from the filesystemedit_file: make selective edits using pattern matching with diff previewIt supports six tools: `list_directory`, `read_file`, `save_file`, `append_file`, `delete_this_file`, and `edit_file` (which offers line-based pattern matching and dry-run previews).
Python 3 (with a virtual environment recommended) and the dependencies installed via `pip install -e .`. The server uses FastMCP and can be run on Windows, macOS, or Linux.
Standard human-readable logs go to the console. Optionally, structured JSON logs can be written to a file specified with `--log-file`. Log level is configurable (DEBUG, INFO, WARNING, ERROR, CRITICAL).
All file paths are normalized and validated to stay within the project directory; path traversal attacks are blocked. Files are written atomically to prevent data corruption, and the `--project-dir` argument is required.
The server communicates via MCP’s standard transport (stdin/stdout) used by Claude Desktop and Cline. No external API endpoints or authentication are mentioned—access is governed by the MCP client’s auto-approval settings.
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📄 The PDF intelligence layer for AI agents — Agent Document Twin, evidence-first extraction, visual crops, OCR provenance, trust reports, and benchmark-gated releases. MCP server for Claude, Cursor, VS Code, and any MCP client.

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