"filesystem" matching MCP servers:

Provides secure filesystem access for AI assistants with optimizations like file reading limits and depth-limited traversal to improve token efficiency. It enables AI models to read, write, and search files within explicitly allowed directories while automatically skipping large system folders.

Node.js server implementing Model Context Protocol (MCP) for filesystem operations with comprehensive permission controls, allowing secure file and directory manipulation with granular access restrictions.

A secure MCP server enabling read-only access and file search capabilities within a specified directory, while respecting .gitignore patterns.

Node.js server implementing Model Context Protocol for secure read-only filesystem operations, allowing Claude to read files, list directories, search files, and get file metadata within specified directories.

Node.js server implementing Model Context Protocol (MCP) for filesystem operations, allowing AI systems to read, write, edit files and manage directories within specified allowed paths.

Enables AI coding agents to safely execute code, run tests, and build projects inside disposable Docker sandboxes, protecting the host machine through enforced isolation, filesystem snapshots, and network controls.

Lets a frontier coding agent delegate research, cataloguing, and long-running computation to a local LLM with guarded filesystem, web, and Python execution tools, preserving the agent's context and tokens.

Provides direct, unsandboxed local machine access via filesystem, Python, Node.js, and shell commands for MCP agents.

Enables executing local commands via an argv-based tool with optional non-interactive sudo, command allowlisting, and filesystem write confinement using Bubblewrap.

Sandboxed bash execution MCP server for AI agents, using an in-memory virtual filesystem overlay to prevent real filesystem damage, with configurable network access, timeouts, and optional Python/JS runtimes.

Enables secure Windows development automation by providing MCP tools for filesystem access, project builds, restricted PowerShell commands, and approved network fetches.

A lightweight MCP server that enables AI assistants to interact with the local machine through terminal, filesystem, and Python execution tools.

An MCP server that provides tools for reading, writing, and editing files on the local filesystem.

A Model Context Protocol server that allows AI to run any Open Source tool via pkgx, enabling Claude to execute commands while maintaining read-only access to the filesystem.

Enables AI assistants to securely inspect, search, edit, build, and test explicitly authorized local development projects over MCP, with sandboxed filesystem access and sensitive-file protection.

A local sandbox that provides AI agents with code execution, filesystem access, and a full GUI desktop environment for 'computer use' capabilities. It exposes tools for shell commands, multi-language code interpretation, and remote desktop automation via the Model Context Protocol.

Enables MCP-capable agents to execute generated Python, Node.js, or shell code inside disposable, network-isolated Docker containers with strict resource, filesystem, privilege, and time limits, returning structured results and errors.

Enables MCP clients to spawn and control Codex CLI and Claude Code sessions on the host machine, with session management and filesystem access.

Execute scientific scripts (R, Python, GMT, LaTeX, Octave, Julia, etc.) in isolated, disposable containers with no host filesystem access.

Exposes a Linux host with shell, Python, and filesystem tools as MCP tools over HTTPS, enabling AI agents to execute commands and manage files remotely.

MCP server that exposes a V8 JavaScript runtime as a tool for AI agents like Claude and Cursor. Supports persistent heap snapshots via S3 or local filesystem, and is ready for integration with modern AI development environments.

Enables secure execution of Python scripts in isolated Docker containers via MCP, with resource limits, package allowlisting, read-only filesystem, and artifact return.

Integration project for Model Context Protocol (MCP) servers with Claude Desktop App, enabling filesystem operations, development support, and file management through natural language.

Enables AI assistants to securely inspect, modify, and manage local projects with trusted filesystem access, safe Git operations, controlled task execution, and developer runtime process management over stdio or HTTP.

Enables coding agents to reach every configured MCP server through just three tools (search, describe, execute) and a sandboxed JavaScript runtime, so a model writes one short program instead of making dozens of tool calls. Runs without network, filesystem, or credential access, returning only the result.

Spins up isolated opencode instances in Docker containers as MCP servers, providing 80 tools for file and shell operations within a scoped workspace without exposing the host filesystem.

Enables AI to run terminal commands with interactive process control, manage long-running processes, and perform filesystem operations including search and diff-based file editing. It also supports reading and editing Excel, PDF, and DOCX files, executing code in memory, and auditing tool-call history.

Executes TypeScript code in a secure sandbox with IPC-bridged access to MCP tools like filesystem operations and network requests.

Provides filesystem access and integration with Z.ai's GLM-4 models for code generation and reasoning tasks. Designed to work as a git submodule with automatic parent repository detection.

A secure MCP gateway for Termux that exposes filesystem, shell, and Termux:API tools over a local HTTP endpoint with admin controls, job management, and upstream support.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Gives AI coding agents typed, mode-gated access to a real WordPress site — content, media, users and settings via core REST, plus PHP execution, SQL queries, filesystem operations and Elementor page read/build/patch/snapshot via a companion bridge plugin. Destructive actions and code execution are guarded by feature flags, explicit confirm literals, dry runs, redaction and audit logging.

Enables controlling a local Scilab/Xcos installation to discover installed block libraries, create native block templates, read, write, and validate diagrams, and run simulations of arbitrary positive duration that return named numerical signal series. It exposes these capabilities as MCP tools over stdio or streamable HTTP, with model file access restricted to configured filesystem roots.

This MCP server gives an AI coding host controlled, client-agnostic access to a local machine's developer capabilities: transactional file edits, shell and PTY execution, Git and project inspection, language-server operations, isolated browser automation, Windows UI automation, VS Code state, durable jobs, and checkpoints, all bounded by filesystem root allowlisting and resource admission controls. It speaks standard MCP over stdio, Streamable HTTP, and legacy HTTP+SSE, and can also run headless as a long-lived job runner without any hosted model vendor.