"Local small language model for filesystem and network tasks" matching MCP servers:

Enables AI assistants to understand lab topologies, follow operational principles, and interact with real network devices through observable terminals while keeping private access information hidden.

An enhanced Model Context Protocol filesystem server that fixes common limitations and adds features like glob pattern search, head/tail reading, intelligent file editing with syntax validation, and new tools for delete and command execution.

Provides advanced filesystem operations for AI-assisted development, including batch file reading, renaming, moving, project structure display, and command execution with Python virtual environment auto-activation.

Aggregates network operations tools such as retrieving local network configurations and scanning ports, enabling IT professionals to perform these tasks via natural language.

A secure MCP server for network diagnostics, providing tools like ping, traceroute, whois, nslookup, dig, nmap, curl, and netstat through natural language interfaces.

Visual network topology editor with AI agent integration via MCP. One-click SSH + web-service access from any node, nmap/CSV import, smart auto-layout, multi-sheet, local-first. Open-source successor to netViz (CA Technologies 1990-2012)

MCP server that enables AI agents to start, monitor, and stop background processes like dev servers, builds, and tests, with a web dashboard.

A local Python MCP server that provides basic tools like greet, add_numbers, and system_info, designed for process-based connections via stdin/stdout.

Enables exploration and search of local filesystems using glob pattern matching to find files and grep to search for text patterns within files.

Exposes VSCode's Language Server Protocol features through MCP, enabling AI assistants to perform language-aware operations like symbol navigation, reference tracking, safe renaming, type information retrieval, and hover documentation across codebases.

Enables cloud-based MCP clients to delegate privacy-sensitive or large-output tasks to a local LLM, returning sanitized placeholders or digested summaries so private data never leaves the user's network.

Enables comprehensive filesystem operations across Linux, macOS, and Windows with platform-aware command execution, secure path restrictions, and intelligent adaptations for file search, directory management, and shell commands specific to each operating system.

Enables ChatGPT to work directly on local projects by reading files, editing code, running tests and commands, inspecting Git state, and managing durable workflows, with optional delegation to local Codex agents for complex tasks.

Enables controlled local developer operations including file and process management, shell execution, SSH, WSL, Docker, Git, browser and desktop automation, media processing, and asynchronous jobs through MCP.

Enables ChatGPT Desktop to read, search, edit and move files on your own computer, produce Word, Excel, PowerPoint, PDF and chart outputs, launch and manage terminal programs and processes, and run work on other machines you own by name over SSH. Everything runs locally under your own setup, with a Docker-isolated, offline document mode or a full-permission mode for file and code operations.

Enables supervised PowerShell execution on Windows, allowing the DeepSeek agent to run local commands and manage files safely through a single tool.

Enables self-hosted local file, terminal, document, and named-device tools for MCP clients, with an isolated document mode and a full-access mode.

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

A single MCP server that orchestrates Expo CLI, Metro, and adb for local Expo project management, including device interaction and session summaries.

Enables ChatGPT Web Developer Mode to securely read, search, list, and edit files inside user-approved local folders, and optionally run approved developer commands on the user's Windows machine.