MCP vs OpenAI Agents SDK: Protocol vs Framework Compared

MCP and the OpenAI Agents SDK solve different layers of the AI integration stack: MCP is an open protocol that standardizes how AI models connect to external tools and data, while the OpenAI Agents SDK is a Python framework for building multi-agent workflows. The two are not competitors -- they operate at different levels of abstraction, and as of early 2025 the OpenAI Agents SDK itself supports MCP as a transport for tool discovery. Understanding where each one fits will help you make the right architectural decisions for your AI applications.

This article breaks down the protocol-versus-framework distinction, compares their architectures side by side, and explains when to use one, the other, or both together.

The Core Distinction: Protocol vs Framework

The single most important concept to grasp is that MCP and the OpenAI Agents SDK are different kinds of things.

Aspect	MCP	OpenAI Agents SDK
Type	Open protocol (specification)	Python framework (library)
Created by	Anthropic	OpenAI
License	MIT open source	MIT open source
Primary purpose	Standardize tool-to-AI connectivity	Orchestrate multi-agent workflows
Language support	Any (TypeScript, Python, Java, Go, etc.)	Python-first
Scope	How tools are exposed and invoked	How agents coordinate and hand off tasks
Analogy	USB-C (the connector standard)	A laptop manufacturer's SDK (builds devices using USB-C)

MCP defines the wire protocol -- the JSON-RPC 2.0 messages, the transport layer, the capability negotiation -- that any AI application uses to discover and call tools on any MCP server. It does not care what framework you use to build your agent.

The OpenAI Agents SDK defines the orchestration layer -- how to create agents with specific instructions, how agents hand off to other agents, how guardrails validate inputs and outputs, and how traces get collected. It does care about tooling, and it now supports MCP servers as a tool source.

For a deeper explanation of the protocol itself, see our comprehensive MCP guide.

Architecture Comparison

MCP Architecture

MCP follows a client-server architecture with three roles:

Host: The AI application the user interacts with (Claude Desktop, Cursor, VS Code, etc.)
Client: A connector inside the host that maintains a 1:1 connection to a specific MCP server
Server: A program that exposes tools, resources, and prompts via the MCP protocol

The communication flow is straightforward:

The client connects to the server and requests its capabilities
The server responds with a list of tools, resources, and prompt templates
The host presents these capabilities to the AI model
The model decides when to invoke a tool and sends a request through the client
The server executes the tool and returns results

MCP supports two transport mechanisms: stdio for local servers (the server runs as a child process) and Streamable HTTP for remote servers (communication over HTTP with optional server-sent events for streaming). For details on these transports, see our MCP architecture guide.

OpenAI Agents SDK Architecture

The OpenAI Agents SDK is built around four core primitives:

Agent: An LLM configured with a system prompt, a set of tools, and optional handoff targets
Handoff: A mechanism for one agent to transfer control to another agent
Guardrail: Validation logic that runs on inputs or outputs to ensure safety and correctness
Tracing: Built-in observability that records every step of agent execution

A typical workflow looks like this:

A "triage agent" receives the user request
Based on the request, it hands off to a specialized agent (coding agent, research agent, etc.)
The specialized agent uses tools to complete the task
Guardrails validate the output before returning it to the user

Tools in the Agents SDK can be Python functions, OpenAI-hosted tools (like code interpreter or file search), or -- and this is the convergence point -- MCP servers.

Tool Definition Comparison

How each system defines and exposes tools reveals their different philosophies.

Defining a Tool in MCP

An MCP server declares tools using JSON Schema. Here is a tool definition from a weather MCP server:

{
  "name": "get_weather",
  "description": "Get the current weather for a city. Returns temperature, conditions, and humidity.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "city": {
        "type": "string",
        "description": "City name, e.g. 'San Francisco'"
      },
      "units": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "description": "Temperature units"
      }
    },
    "required": ["city"]
  }
}

The tool is defined once on the server. Any MCP client -- regardless of the AI model or host application behind it -- can discover and invoke it.

Defining a Tool in the OpenAI Agents SDK

In the Agents SDK, tools are typically Python functions decorated with metadata:

from agents import Agent, function_tool

@function_tool
def get_weather(city: str, units: str = "celsius") -> str:
    """Get the current weather for a city. Returns temperature, conditions, and humidity."""
    # implementation here
    return f"Weather in {city}: 72F, sunny"

agent = Agent(
    name="Weather Agent",
    instructions="You help users check the weather.",
    tools=[get_weather],
)

The function signature and docstring are automatically converted into a tool schema that gets sent to the OpenAI model. This is convenient but tightly coupled to the Python framework and the OpenAI API.

Side-by-Side Comparison

Feature	MCP Tool	Agents SDK Tool
Definition format	JSON Schema (language-agnostic)	Python function + decorator
Discovery	Dynamic at runtime via protocol	Static at agent configuration
Reusability	Any MCP client can use it	Tied to the Agents SDK
Schema source	Explicit JSON Schema	Inferred from type hints
Execution	Server-side (runs in the MCP server process)	Local (runs in the agent process)
Language	Any	Python only

When to Use Each

Use MCP When...

You want tool reusability: You build a tool once (e.g., a GitHub integration) and want it to work with Claude Desktop, Cursor, VS Code, ChatGPT, and any future MCP-compatible client
You are building a tool library: Your organization wants a catalog of internal tools that any AI application can consume
You need language flexibility: Your tools are written in Go, Java, Rust, or anything other than Python
You care about open standards: You want to avoid vendor lock-in and invest in a protocol that multiple AI vendors support
You want local-first security: You need tools that run on the user's machine with explicit permission grants

Use the OpenAI Agents SDK When...

You are building a multi-agent system: You need agents to hand off tasks to each other with shared context
You need guardrails: You want structured input/output validation on agent behavior
You want built-in tracing: You need detailed execution logs for debugging and compliance
Your stack is Python-centric: Your team works primarily in Python and wants a batteries-included framework
You are deeply invested in OpenAI models: Your application uses GPT-4o or o3 and you want native OpenAI API integration

Use Both Together When...

The most powerful approach is often to combine them. The OpenAI Agents SDK added native MCP support, allowing agents to discover and use tools from MCP servers. This means you can:

Build tools as MCP servers (reusable, language-agnostic, open standard)
Orchestrate agents with the Agents SDK (multi-agent handoffs, guardrails, tracing)
Connect the agents to MCP servers for tool access

from agents import Agent
from agents.mcp import MCPServerStdio

# Connect to an MCP server for GitHub tools
github_server = MCPServerStdio(
    command="npx",
    args=["-y", "@modelcontextprotocol/server-github"],
    env={"GITHUB_TOKEN": "your-token"}
)

agent = Agent(
    name="Dev Agent",
    instructions="You help with GitHub tasks.",
    mcp_servers=[github_server],
)

In this pattern, the MCP server provides the tools while the Agents SDK provides the orchestration. You get the best of both worlds.

The Convergence Trend

The relationship between MCP and the OpenAI Agents SDK illustrates a broader industry convergence. When MCP launched in November 2024, OpenAI had its own function calling approach and no MCP support. By March 2025, OpenAI shipped native MCP support in the Agents SDK. By mid-2025, MCP support was expanding across the OpenAI product line.

This convergence pattern is repeating across the industry:

Company	Initial Approach	MCP Support Added
Anthropic	Created MCP	November 2024
OpenAI	Function Calling, Plugins	March 2025 (Agents SDK)
Google	Gemini Extensions	2025 (via A2A + MCP bridge)
Microsoft	Copilot Plugins	2025 (VS Code, Copilot)
Cursor	Custom tool system	Early 2025

The takeaway: MCP is becoming the standard tool-connectivity layer, while frameworks like the Agents SDK build orchestration on top of it. Investing in MCP servers future-proofs your tools. Choosing an orchestration framework is a separate decision that depends on your model preferences and workflow complexity.

For a comparison of MCP with Google's complementary protocol, see MCP vs Google A2A.

Key Technical Differences

Dimension	MCP	OpenAI Agents SDK
Communication	JSON-RPC 2.0 over stdio or Streamable HTTP	Python function calls + OpenAI API
State management	Stateful sessions per connection	Managed by the Runner loop
Authentication	OAuth 2.1 for remote servers	API key for OpenAI, custom for tools
Streaming	Native via SSE / Streamable HTTP	Via OpenAI streaming API
Error handling	JSON-RPC error codes	Python exceptions + guardrails
Capability negotiation	Built into protocol handshake	Not applicable (static config)
Model agnostic	Yes (any AI model)	Primarily OpenAI models (configurable)

Making the Decision

The decision is not MCP or the OpenAI Agents SDK. It is MCP and/or the OpenAI Agents SDK, depending on what you are building:

Building a tool that should work everywhere? Build an MCP server. Browse our server directory for examples and inspiration.
Building a multi-agent application with OpenAI models? Use the Agents SDK and connect it to MCP servers for tools.
Building a simple single-agent app? You might not need the Agents SDK at all -- just connect an MCP client to the servers you need.

The protocol layer (MCP) and the orchestration layer (Agents SDK) are complementary. Understanding this distinction helps you invest in the right abstractions at each level of your AI stack.