Agent MCP

Agent MCP provides a framework for coordinating multiple AI agents through the Model Context Protocol. Rather than having a single agent handle everything, it lets you define specialized agents that collaborate on complex tasks — one agent researches, another writes code, a third reviews. The MCP server acts as the orchestration layer, managing task delegation, state sharing, and result aggregation. The core idea is that complex workflows benefit from division of labor between agents, each with their own context window, tools, and system prompts. Agent MCP provides the plumbing: task queues, agent communication channels, shared memory, and progress tracking. Your AI agent can spawn sub-agents, assign them work, and collect results — all through standard MCP tool calls. With 1,100+ stars and active development, it has gained traction among developers building agentic applications. The architecture is well-thought-out, but the learning curve is steeper than a typical MCP server. You need to understand both the MCP protocol and multi-agent patterns to use it effectively. It is best suited for power users building sophisticated AI workflows, not casual tool integration.