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

A local, offline MCP server that enables AI agents to collaborate with SketchUp for controlled modeling, editing, and review tasks via 41 MCP tools.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

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

A comprehensive and efficient Model Context Protocol server for task management that works with Claude, Cursor, and other MCP clients, providing powerful search, filtering, and organization capabilities across multiple file formats.

Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.

Enables AI assistants to read, write, and manage files on the local system with security features like path restrictions and optional read-only mode.

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

A Model Context Protocol server that gives AI assistants secure, sandboxed filesystem access with tools for reading, writing, searching, and managing files and directories.

Enables AI assistants to securely browse, search, inspect, and understand local project files through Model Context Protocol tools.

MCP server for performing filesystem operations, enabling file management and manipulation through natural language.

Standalone TypeScript MCP server for filesystem operations with robust security, encoding support, and cross-platform path handling.

MCP server for agentic task orchestration and management, enabling AI agents to create goals, plan tasks with acceptance criteria, track dependencies, request human input, and provide proof of completion.

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.

A token-optimized filesystem MCP server that groups 14 tools into 6 semantic operations, enabling file creation, deletion, reading, moving, querying, and more with reduced context token consumption.

A professional MCP server that provides filesystem operations while automatically respecting .gitignore patterns, enabling efficient and token-friendly file access for Claude.

Task manager for AI agents with projects, priorities, and status tracking. Reads are free, writes require Veyra commit mode.

Enables reading, creating, and editing files on the local filesystem through operations like view, create, string replacement, and line insertion.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

Provides basic filesystem operations such as listing directories, reading/writing files, retrieving file info, and searching for files, enabling natural language file management.

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.