Model Context Protocol (MCP)
Layer 3 — Runtime Protocol: MCP and Multi-Agent Orchestration
Once your agent has instructions and tools, the next question is: how does it talk to the world? This is where protocols come in.
Model Context Protocol (MCP)
MCP, open-sourced by Anthropic in late 2024 and now stewarded by the Linux Foundation’s Agentic AI Foundation, is the closest thing the AI ecosystem has to a universal plug standard. Think of it as USB-C for AI integrations.
The architecture in one paragraph: An MCP server exposes three kinds of objects — resources (data, like a file or a database row), tools (actions, like running a query), and prompts (templated workflows). An MCP client (your agent) connects to one or more servers, discovers what is available, and invokes resources and tools as needed. Communication uses JSON-RPC 2.0 over stdio (local) or HTTP + Server-Sent Events (remote).
Agent (MCP client)
│
├── MCP Server: filesystem (resources: files; tools: read, write)
├── MCP Server: GitHub (resources: repos, PRs; tools: create_pr, comment)
└── MCP Server: Postgres (resources: tables; tools: run_query)Claude, Cursor, and an expanding list of platforms support MCP out of the box. The official MCP registry lists hundreds of pre-built servers for GitHub, Slack, Google Drive, databases, and more.
→ MCP specification & SDK docs → MCP server registry → Anthropic — MCP quickstart
Multi-agent orchestration — OpenAI Agents SDK
For applications that genuinely need multiple specialised agents, OpenAI’s Agents SDK (the successor to the experimental Swarm framework) provides two main patterns:
Handoffs — one agent transfers full control to another:
from agents import Agent, Runner
billing_agent = Agent(
name="Billing",
instructions="Handle billing questions only."
)
triage_agent = Agent(
name="Triage",
instructions="Route questions to the right specialist.",
handoffs=[billing_agent]
)
result = Runner.run_sync(triage_agent, "Why was I charged twice?")Agents-as-tools — a manager agent calls specialists for sub-tasks and retains control of the final response. Choose this when you need to compose results from several specialists into a single answer.