# AI Multi Agent MCP

> 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: `ebarti/ai-multi-agent-mcp` (Agent Skill, multi-file: 18 files)
- Install (CLI): `npx skillmds@latest add ebarti/ai-multi-agent-mcp`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ebarti/ai-multi-agent-mcp/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ebarti (https://skillmd.com/u/ebarti)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ebarti/ai-multi-agent-mcp

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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

| Category | Purpose |
|----------|---------|
| `mcp-protocol` | Simplified envelope format, official-MCP caveats, transports, OpenAI client init, MAS workflow |
| `agent-design` | Specialist agent template (Researcher, Writer), helper function pattern |
| `orchestration` | Orchestrator role, hub-and-spoke routing, goal decomposition |
| `robustness` | Robust LLM components, envelope validation, Validator agent, validation loop |

### Workflows

| Workflow | Purpose |
|----------|---------|
| `workflows/build-mas.md` | Build a multi-agent system from scratch (envelope → agents → orchestrator → first run) |
| `workflows/harden-mas.md` | Add validation loops, robust LLM components, Validator agent |

## Guidelines

See `guidelines.md` for task-based file selection.