"files and folders" matching MCP servers:

Connects AI agents to microsandbox for creating lightweight sandboxes, executing code, managing files, and monitoring resources.

An MCP server that gives an agent terminal and file-management capabilities, including running shell/Python commands, searching and reading files, and creating/editing/deleting files and folders.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

Enables AI agents to run shell commands, execute Python code, read and write files, search text, and create or delete folders through MCP stdio tools.

Enables running multiple Claude prompts simultaneously in parallel with support for file contexts and output redirection to individual files.

Enables AI agents to execute terminal commands and Python scripts, search and manage files, and create or delete folders through the Model Context Protocol.

An MCP server built with FastMCP that provides basic terminal abilities like running commands, running Python, and working with files and folders.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

MCP server for secure, session-based Python code execution in Docker containers, enabling LLM applications to run code, manage state, and access files.

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

An MCP server that allows Claude Desktop to remotely control Claude Code instances for executing commands and managing files.

Enables chat clients to execute real shell commands (apt install, curl, ffmpeg, Python) inside a disposable, per-conversation Debian container and hand resulting files back to the user via signed download links. Installed packages and processes are discarded when the container recycles, while files under /workspace persist.

Enables AI coding agents to create disposable, isolated Firecracker MicroVM sandboxes on AWS Lambda, execute commands, transfer files, and tear them down when done.

A secure, container-based implementation of the Model Context Protocol (MCP) that provides sandboxed environments for AI systems to safely execute code, run commands, access files, and perform web operations.

Provides a persistent Python REPL session as a tool for executing code, managing files, installing packages, and initializing projects via the MCP protocol.

An experimental server implementing the Model Context Protocol to allow AI assistants to query and analyze financial data stored in Beancount ledger files using the Beancount Query Language.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

A hot-reloadable MCP proxy server that enables users to create and manage custom Python tools through dynamic module loading. Users can build their own utilities, wrap APIs, and extend functionality by simply adding Python files to designated folders.

Repomix MCP Server enables AI models to efficiently analyze codebases by packaging local or remote repositories into optimized single files, with intelligent compression via Tree-sitter to significantly reduce token usage while preserving code structure and essential signatures.

A Model Context Protocol server that connects Claude and other MCP clients to Aider, enabling AI assistants to efficiently edit files, create new files, and interact with git repositories through natural language.