When you read agent docs, three names keep appearing. MCP, Agents API and SDK, WebMCP.

"So what should I learn?" is a fair question. Here is the short answer first. They are not rivals. They play different roles.

MCP        = connection standard (how AI attaches to external tools)
Agents SDK = runtime (how turns, tools, and delegation run)
WebMCP     = web exposure (how web apps expose features to AI)

Let us unpack each one.

MCP: the USB-C for AI

The Model Context Protocol is an open standard for AI apps to attach to outside systems. The official docs use a clear metaphor. Like USB-C, once you match the shape, you can plug it in many places.

It has three parts:

Host (AI apps like Claude, ChatGPT, VS Code, Cursor)
  └─ Client (handles the connection)
      └─ Server (exposes your data, tools, and workflows)

Build one MCP server, and calendars, docs, databases, search, and calculators become usable across many AI apps. Typical cases include Claude Code turning a Figma design into a full web app, or driving 3D work in Blender.

The core idea is this. MCP standardizes "what you can ask for." How execution runs is not MCP's concern.

Agents SDK: who runs it

The OpenAI Agents SDK is a runtime for running agents. Its basic parts are small.

Agent (instructions + tools)
  + delegation (hand off to another agent)
  + guardrails (input and output checks)
  + sessions and tracing (memory and debugging)

The official docs draw a clean line:

When to use the Responses API directly:
- You want to own loops, tool branching, and state
- Short, simple calls are the core

When to use the Agents SDK:
- You want the runtime to own turns, tool runs, guardrails, and delegation
- You need multi-step deliverables
- You want to run in an isolated workspace (sandboxed agents)

This matches the split in Agents API vs your own agent loop and the OpenAI Agents API article. It is a question of how much you delegate and how much you build yourself.

The smallest example looks like this:

from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

Then you attach a function tool, delegate to another agent when needed, add guardrails, and watch the flow with tracing. MCP server tools can attach to the agent too. So MCP and the SDK do not overlap. They connect.

WebMCP: the web developer's turn

As covered in the Meta Ray-Ban Display and WebMCP article, WebMCP is a way to expose web app features to AI, including on-device AI like glasses.

From a web developer view:
My web app features (booking, lookup, ordering, control)
  → expose with WebMCP
    → callable from AI glasses and AI apps
      → "a web developer can build AI-glasses apps"

If MCP is the general connection standard, WebMCP is the practical path for web apps to expose their features in that standard or a similar way. For web developers, it has the lowest entry barrier. You can build AI-connected apps with web skills you already have.

A picker: what should you grab first

Situation Grab first Why
I want to attach my DB, docs, or internal tools to AI MCP server Build once, reuse in many apps
I want to run multi-step work automatically Agents SDK It owns turns, delegation, and guardrails
I want AI to call my web app features WebMCP Start directly with web skills
I want to attach a coding agent to my project Harness + MCP Read with the harness article
I want voice plus real tool runs Realtime voice + MCP Read with the Gemini Live article 
Suggested order (for most people):
1. Expose 1 tool with MCP as a standard (read-only first)
2. Run a 2-step flow in Agents SDK (tool call + check)
3. Add 1 delegation (pass review to another agent)
4. Expose with WebMCP when web access is needed

As the Qwen Code subagent article shows, starting small with delegation builds your orchestration sense fast.

CodeBridge Mini Lab: get the feel in 1 hour

1. One MCP server (30 min):
   - One read-only tool (for example: internal doc search)
   - Check that Claude or Cursor can attach

2. One Agents SDK flow (20 min):
   - Run the example above → add 1 function tool → check run logs

3. One delegation (10 min):
   - Split into draft agent + review agent and compare results
   - Record total cost and time

Apply layer 1, scope, from the 4-layer security article here too. Read-only first is the rule.

Conclusion: store them as standard, runtime, and exposure

One more recap:

MCP is the connection standard, Agents SDK is the runtime, WebMCP is the web exposure path.

This is not a pick-one test. Expose tools with MCP, run flows with the SDK, and expose through the web when needed. Your task today is one thing. Ship one read-only tool as a standard. That one tool becomes your first asset in the agent era.

Further reading

References

Go deeper with a course

If you want to move past exposing tools into agent structures with delegation and checks, stack harness, loop, and graph skills in order.