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Ai multi agent mcp

Skill ebarti/skills/ai-multi-agent-mcp

📚 Agent skills distilled from technical books — AI Engineering, Context Engineering, Designing Data-Intensive Applications, and more. Agent-agnostic, plain Markdown. Give your AI agent a bookshelf.

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npx -y skills add ebarti/skills --skill ai-multi-agent-mcp

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Building multi-agent systems with a simplified inter-agent message envelope inspired by MCP-like structured messaging. This is not the official Model Context Protocol (MCP), which is a host/client/server protocol using JSON-RPC 2.0 with lifecycle initialization, capability negotiation, and server features such as tools/resources/prompts. Covers the book's pedagogical envelope format and transport choices, specialist agent design (Researcher / Writer + shared helpers), Orchestrator design with goal decomposition, and robustness via validation loops + a Validator agent. Use this skill when: - Designing inter-agent communication (simplified envelope schema, transport choice) - Building specialist agents (one role per agent, system prompt as identity) - Implementing an Orchestrator that routes between agents - Adding validation loops, retries, or a Validator agent - Hardening a multi-agent prototype against LLM/network/data failures

SKILL.md

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AI Multi-Agent MCP

Knowledge from "Context Engineering for Multi-Agent Systems" (Chapter 2). The chapter's create_mcp_message examples are a simplified inter-agent message envelope, not official MCP. Official MCP is a host/client/server protocol over JSON-RPC 2.0 with initialization/capability negotiation and server features such as tools/resources/prompts.

Quick Start

  1. Check guidelines.md to find which files to load
  2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
  3. Apply guidance to your work

Contents

References

CategoryPurpose
mcp-protocolSimplified envelope format, official-MCP caveats, transports, OpenAI client init, MAS workflow
agent-designSpecialist agent template (Researcher, Writer), helper function pattern
orchestrationOrchestrator role, hub-and-spoke routing, goal decomposition
robustnessRobust LLM components, envelope validation, Validator agent, validation loop

Workflows

WorkflowPurpose
workflows/build-mas.mdBuild a multi-agent system from scratch (envelope → agents → orchestrator → first run)
workflows/harden-mas.mdAdd validation loops, robust LLM components, Validator agent

Guidelines

See guidelines.md for task-based file selection.

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