mcp-server

What problem does it solve?

Coordinating complex tasks across multiple AI agents requires a robust communication and management protocol, which can be challenging to implement and maintain. This Skill simplifies multi-agent orchestration.

Core Features & Use Cases

• Multi-Agent Communication: Manage and interact with a Multi-Agent Communication Protocol (MCP) server, enabling seamless communication between AI agents.
• Distributed Task Execution: Orchestrate complex multi-agent workflows and distributed task execution across various AI components.
• Monitoring & Control: Monitor and control distributed AI tasks, ensuring efficient and coordinated operation.
• Use Case: Set up a system where one AI agent gathers data, another analyzes it, and a third generates a report, all communicating seamlessly through the MCP server to complete a complex business process.

Quick Start

This skill provides tools to interact with an MCP server.

To check active connections (conceptual, requires a running server):

from scripts.connections import get_active_connections

active_agents = get_active_connections()

print(f"Currently connected agents: {active_agents}")

For detailed instructions on setting up and interacting with an MCP server,

refer to the 'reference/' directory, specifically 'python_mcp_server.md'

or 'node_mcp_server.md'.