mcp-coordination

What problem does it solve?

Coordinating multiple AI agents in a swarm, sharing their status, findings, and handing off tasks, is a complex challenge. This skill provides standardized patterns for inter-agent communication, enabling efficient, distributed decision-making and task execution.

Core Features & Use Cases

• Store/Retrieve Agent Status: Agents can update their idle, working, blocked, completed, or failed status, along with current task and progress.
• Share Findings Between Agents: Facilitates knowledge transfer, allowing a "researcher" agent to share insights with a "planner" agent.
• Broadcast to All Agents: Enables system-wide announcements, such as architecture decisions or critical errors.
• Coordinate Task Handoff: Streamlines the transfer of tasks from one agent to another, like a "coder" handing off to a "tester."
• Use Case: A "project manager" agent can monitor the progress of multiple "coder" agents, and if one gets "blocked" due to a database issue, a "devops" agent can be notified to resolve it.

Quick Start

Store the current status of 'coder-001' as 'working' on 'implementing authentication API'.