MCP vs OpenAI Functions vs ChatGPT Plugins: Choosing the Right Integration Method

TL;DR If you need a fast, future-proof way to make any API usable by AI agents across vendors, choose MCP and ship it via MCPify. If you only need a couple of narrow actions inside an OpenAI-only app, OpenAI function calls are fine. ChatGPT plugins were useful historically but are now deprecated in favor of GPTs and Actions, so treat them as legacy.

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Why this comparison matters

Modern AI assistants don't live on an island. They must read from CRMs, write tickets, search docs, place orders, and trigger workflows. There are three prominent ways teams have tried to connect external systems to LLMs:

• MCP (Model Context Protocol), a cross-vendor open standard that exposes tools and data to AI in a transparent, schema-rich, stateful way.
• OpenAI function calls, a convenient, OpenAI-specific mechanism for calling developer-defined functions from within GPT-5 conversations.
• ChatGPT plugins, the now-retired mechanism that briefly let third parties expose web APIs directly in ChatGPT's UI.

Below we compare how they work, their pros and cons, what it takes to build with each, and when to choose which.

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What is MCP (and MCPify)?

MCP is an open protocol that standardizes how AI agents discover, call, and reason about external tools and data sources. Think of MCP as USB-C for AI: one consistent way to connect assistants to many systems. It's supported across the industry, with official docs and SDKs, and native support emerging in major AI platforms.

• Overview: Introducing the Model Context Protocol
• Docs: modelcontextprotocol.io

MCPify is the fastest way to adopt MCP. It transforms any REST, GraphQL, or proprietary API into an AI-ready MCP service. You provide a minimal JSON config or spec, and MCPify generates a fully described, secure, cached, rate-limit-aware MCP service that AI agents can use.

• Site: mcpify.org
• Docs: mcpify.org/docs

Why MCP stands out

• Open, model-agnostic: One MCP service can be used by multiple assistants and LLMs, not only GPT-5.
• Rich transparency: Full parameter and response schemas, plus metadata on pagination, limits, costs, and latency.
• Statefulness: Long-lived sessions for multi-step workflows and partial-result navigation.
• Fine-grained data control: Let the agent fetch only what it needs via pagination, slicing, field selection, and JSON navigation.
• Standardized auth: OAuth-first flows and uniform permissions.
• First-class support: Major vendors now integrate remote MCP servers directly in their APIs.

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OpenAI function calls (aka tool calling inside the Responses API)

OpenAI's function calling lets GPT-5 propose a tool call with structured arguments that your backend executes. You define the function schema; the model decides when to call it; your code runs and returns results.

Why teams pick function calls

• Simple for small scope: Add one or two capabilities quickly to an OpenAI-powered app.
• Good structure: Arguments arrive as well-formed JSON.
• Server-side control: You hold the keys and validate execution.

Important constraints

• OpenAI-specific: Ties your integration to a single vendor's API.

• Pre-declare tools per session: Harder to scale to lots of endpoints.

• Statelessness: Multi-step flows and pagination orchestration are manual.

• Token overhead: Large tool definitions consume context on every call.

• Docs: Function calling guide

• Responses API updates and tool support (including MCP): New tools and features in the Responses API

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ChatGPT plugins (legacy)

Plugins were OpenAI's early 2023 mechanism to let ChatGPT call third-party APIs described via OpenAPI. They demonstrated that LLMs can read specs and call web APIs, but the approach was sunset in 2024 in favor of GPTs and Actions.

• Historical page: ChatGPT plugins announcement
• Deprecation timing noted by partners: Zapier plugin sunsetting notice
• Ongoing product direction: ChatGPT Release Notes

Treat plugins as history that informed today's standards like MCP.

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Side-by-side comparison

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Development effort: what it actually feels like

1) Exposing an API with MCPify

Minimal JSON is enough. MCPify generates rich tool descriptions, schemas, pagination controls, caching, and OAuth handling for you.

{
  "service_name": "your-api",
  "base_url": "https://api.your-service.com/v1",
  "auth_type": "oauth2",
  "tools": {
    "list_items": {
      "description": "List items with filtering",
      "endpoint": "/items",
      "method": "GET",
      "query": {
        "search": {"type": "string"},
        "limit": {"type": "integer", "default": 25}
      }
    },
    "create_item": {
      "description": "Create a new item",
      "endpoint": "/items",
      "method": "POST",
      "body_schema_ref": "#/components/schemas/NewItem"
    }
  }
}

Once onboarded, your API is available as an MCP server with exhaustive metadata and logging.

• Learn more: MCPify Quick Start

2) Calling a remote MCP server from GPT-5 (OpenAI Responses API)

Modern OpenAI tooling lets GPT-5 call remote MCP servers directly, so you can keep one standard integration that works across assistants.

# Python (conceptual example)
from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5",  # use your GPT-5-capable model identifier
    tools=[{
        "type": "mcp",
        "server_label": "inventory",
        "server_url": "https://inventory.mcp.mcpify.org/mcp"  # your MCPify endpoint
    }],
    input="Find the SKU for the 'Nimbus Hoodie' in size M and add it to my cart."
)

print(response.output_text)

• Reference: New tools and features in the Responses API

3) Adding a single capability with OpenAI function calls

Function calls are great when you only need 1-3 narrowly scoped actions inside an OpenAI app.

# Python (conceptual example)
from openai import OpenAI

client = OpenAI()

tools = [{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Return current weather for a city.",
    "parameters": {
      "type": "object",
      "properties": {"city": {"type": "string"}},
      "required": ["city"]
    }
  }
}]

resp = client.responses.create(
    model="gpt-5",  # your GPT-5-capable model
    tools=tools,
    input="Do I need an umbrella in Oslo today?"
)

# Your server should inspect tool calls in resp and execute `get_weather`
# then pass the result back into a follow-up responses.create() call.

• Guide: Function calling

4) ChatGPT plugin manifest (historical example)

Plugins are no longer the recommended path, but for context, a minimal manifest looked like:

{
  "schema_version": "v1",
  "name_for_human": "Weather",
  "name_for_model": "weather",
  "description_for_model": "Get current weather for a city.",
  "auth": { "type": "none" },
  "api": {
    "type": "openapi",
    "url": "https://example.com/openapi.yaml"
  }
}

• Historical page: ChatGPT plugins

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Security and compliance

• MCP (with MCPify)

  • OAuth-first with clean scope control across services.
  • Transparent tool metadata makes it easier to audit who can do what.
  • Gateway-first architecture centralizes rate limits, cost controls, and logging.
  • Response chunking and field filtering mean you never over-share data by accident.

• OpenAI function calls

  • You own the enforcement in your code: validate inputs, check permissions, throttle, and audit.
  • Works well when the action surface is very small and you prefer custom, code-level guardrails.

• ChatGPT plugins (legacy)

  • User-consent heavy, but auth patterns were inconsistent and it is deprecated.
  • Modern replacements are GPTs with Actions and remote tool support.

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Which should you choose?

Choose MCP (via MCPify) if you:

• Need one integration standard across multiple LLMs and assistants.
• Expose many endpoints or orchestrate multi-step workflows.
• Care about structured transparency (schemas, limits, cost hints) and observability.
• Want to ship via configuration instead of bespoke glue code.

Choose OpenAI function calls if you:

• Are already building a narrow OpenAI-only app.
• Need just a couple of deterministic backend actions.
• Prefer writing and owning the execution logic directly in your service.

Treat ChatGPT plugins as legacy:

• They taught us valuable patterns, but new development should use MCP or OpenAI Actions/Responses with built-in tool support.

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Practical decision checklist

• Scope size

  • 1-3 actions only? Start with function calls.
  • 10+ endpoints or multi-system workflows? Use MCP via MCPify.

• Vendor strategy

  • Multi-model, multi-assistant future? MCP.
  • OpenAI-only app? Function calls are fine.

• Governance

  • Need standardized OAuth, global rate limits, and cost visibility? MCPify gateway.
  • OK to handcraft checks per action? Function calls.

• Time to value

  • Need one standard across many APIs without writing wrappers? MCPify.
  • Prototyping a single helper function? Function calls.

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Call to action

• Make any API AI-ready: talk to sales
• See how tool descriptions and data navigation work: MCPify Docs
• Talk to an expert about your stack: Book a demo

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Sources

• Anthropic announcement: https://www.anthropic.com/news/model-context-protocol
• MCP official docs: https://modelcontextprotocol.io/docs/getting-started/intro
• MCP GitHub organization: https://github.com/modelcontextprotocol
• OpenAI Responses API adds remote MCP server support: https://openai.com/index/new-tools-and-features-in-the-responses-api/
• OpenAI function calling guide: https://platform.openai.com/docs/guides/function-calling
• OpenAI Actions (for GPTs) overview: https://platform.openai.com/docs/actions/introduction
• ChatGPT plugins announcement (historical): https://openai.com/index/chatgpt-plugins/
• ChatGPT plugins deprecation timing noted by partner: https://help.zapier.com/hc/en-us/articles/24785309335565-Sunsetting-the-Zapier-ChatGPT-plugin-what-you-need-to-know
• ChatGPT Release Notes (product evolution, GPTs and Actions): https://help.openai.com/en/articles/6825453-chatgpt-release-notes
• MCPify home: mcpify.org
• MCPify documentation: /docs