How do I install the AI Multi Agent MCP skill?

Run npx skillmds@latest add ebarti/ai-multi-agent-mcp in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Multi Agent MCP safe to use?

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Which AI agents work with AI Multi Agent MCP?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Multi Agent MCP free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Multi Agent MCP?

ebarti (@ebarti) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Multi Agent MCP by ebarti — Agent Skill

Name: AI Multi Agent MCP
Author: ebarti

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

ebarti 2cfe0ff 18 files · 93.9 KB Updated Sep 17, 2026

File contents

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

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

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.

---name: ai-multi-agent-mcpdescription: 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---# AI Multi-Agent MCPKnowledge 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 Start1. Check `guidelines.md` to find which files to load2. 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 |## GuidelinesSee `guidelines.md` for task-based file selection.

Frequently asked questions