"Filesystem" matching MCP servers:

A minimal MCP server that exposes three file operations (read_file, write_file, list_directory) within a sandboxed workspace, enabling file management via natural language.

MCP Server that enables LLMs to interact with the local filesystem.

Enables reading, writing, appending, and listing files, restricted to explicitly allowed directories with safety checks against path traversal and symlink escapes.

Enables AI coding agents to watch directories for live filesystem changes (create, modify, delete, move) via the Model Context Protocol, providing event-driven access without busy-polling.

Exposes Linux filesystem operations as tools for LLM agents, with configurable root directory and read-only mode, built with FastMCP.

Enables advanced file system operations such as file read/write, directory management, search, archiving, and file watching through natural language.

A secure, read-only MCP server for browsing and searching files in a specified directory with path traversal protection and .gitignore support.

Provides local hybrid search over files by combining semantic vector and full-text BM25 retrieval, with MCP stdio/HTTP support, OAuth 2.1 authentication, and tools for reading and converting documents to Markdown.

Enables any MCP-compatible client to perform filesystem operations — reading, listing, writing, searching, watching directories, and batch processing files — over JSON-RPC 2.0 stdio transport. Includes resource discovery, metrics tracking, and security controls such as allowed roots, size limits, and format allowlists, with a LangGraph agent that accesses files exclusively through MCP.

Provides desktop AI clients with sandboxed, controlled read, write, search, and management access to user-selected local folders and projects over the Model Context Protocol.

A safe MCP server for sandboxed filesystem operations (list, move, create directories, delete files) via pure Python, designed for Claude Desktop.

An MCP memory server for coding agents that checks stored facts against the filesystem and git before relying on them, flagging stale memories.

Enables AI assistants to interact with local Obsidian vaults through direct filesystem access for reading, creating, and managing notes. It features high-performance SQLite indexing for fast searches, regex support, and tools for organizing tags and links without requiring additional plugins.

MCP server that vets LLM-emitted shell commands BEFORE execution — detects rm -rf nested deep in chains, package-manager glob removal (apt remove 'nvidia'), dd/mkfs filesystem destruction, chmod 777 / chown -R privilege blast, network-exfil via curl | bash, chained shutdown/reboot, git destructive ops. 30 detection rules across 8 families. Sub-second, local, free, MCP-native.

Serves an LLM-maintained markdown wiki to agents over MCP, providing tools for querying and navigating wiki pages without direct filesystem access.

Local inter-agent messaging for AI coding agents via filesystem relay.

Enables AI agents to interact with Kubernetes clusters through progressive disclosure, where agents discover TypeScript modules via filesystem, write and execute code, and receive summarized console output for cluster management tasks.

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.

Enables natural-language diagnosis of Linux system issues by providing sandboxed, typed tools for system, service, filesystem, and git inspection, with human approval for mutating actions.

Enables local stdio-based interaction with SingleStore databases from MCP clients, letting users run SQL, inspect schemas, and manage SingleStore Pipelines for continuous data loading from S3, Kafka, Azure, GCS, or filesystem sources.