# Task Decomposition

Skynet - MCP is a hierarchical AI agent network project based on the Model Context Protocol (MCP), supporting the creation of sub - agents to process complex tasks in parallel, integrating multiple AI models and service tools, and providing functions such as dual - mode operation, tool discovery, and asynchronous execution.

The OpenRouter Agents MCP Server is a research agent server based on the Model Context Protocol (MCP). It uses Claude 3.7 Sonnet as the planning agent to coordinate multiple OpenRouter models to decompose and execute complex research tasks, providing efficient and reliable AI research services.

The Ollama-MCP-server is a protocol server that connects local Ollama LLM instances with MCP-compatible applications, providing functions such as task decomposition, result evaluation, and model management, and supporting standardized communication and performance optimization.

Speelka Agent is a general LLM agent based on the Model Context Protocol (MCP), capable of invoking tools from other MCP servers to achieve complex task decomposition, multi - model collaboration, and tool orchestration.

A multi - agent browser automation framework based on AgentKit, which realizes intelligent web page navigation and task execution through division of labor and collaboration, including four core modules: task planning, navigation control, browser operation and result verification.

The Divide and Conquer MCP Server is a JSON - structured task management tool that helps AI agents break down complex tasks into manageable subtasks and provides progress tracking and context saving functions.