MCP vs OpenAI Plugins - Protocol Comparison & When to Use Each

MCP and OpenAI Plugins: Two Approaches to AI Tool Integration

Both the Model Context Protocol (MCP) and OpenAI Plugins solve the same fundamental problem: giving AI assistants the ability to interact with external tools, data, and services. However, they take fundamentally different approaches. Understanding these differences is crucial for developers choosing which ecosystem to build for and users deciding which tools to invest in.

MCP is an open protocol created by Anthropic that works locally, supports multiple AI clients, and gives users full control over their tools. OpenAI Plugins (now evolved into GPT Actions and the ChatGPT plugin ecosystem) are a proprietary system tied to OpenAI's platform.

This guide provides a thorough, neutral comparison. For an introduction to MCP itself, see our What is MCP tutorial.

Architecture Comparison

Aspect	MCP	OpenAI Plugins / GPT Actions
Protocol type	Open standard (MIT license)	Proprietary (OpenAI-controlled)
Execution model	Local (runs on your machine)	Cloud (runs on vendor servers)
Client support	Claude Desktop, Cursor, VS Code, Windsurf, Cline, 15+ more	ChatGPT only
Data privacy	Data stays local (stdio transport)	Data passes through OpenAI servers
Transport	stdio (local) or Streamable HTTP (remote)	HTTPS API calls
Tool definition	JSON-RPC with Zod/JSON schemas	OpenAPI specification
Authentication	Local env vars, no auth needed for stdio	OAuth2 or API key (OpenAI-managed)
Capabilities	Tools, Resources, Prompts, Sampling	API actions only
Distribution	npm/pip packages, GitHub repos	OpenAI GPT Store
Approval process	None (open ecosystem)	OpenAI review required
Ecosystem size	5,000+ servers (growing rapidly)	1,000+ GPTs with actions
Offline capability	Yes (stdio works offline)	No (requires internet)

Open Protocol vs Proprietary Platform

The most fundamental difference is that MCP is an open standard while OpenAI Plugins are proprietary. This affects every downstream decision:

MCP is open: The protocol specification is public, the SDKs are MIT-licensed, and anyone can build a client or server without permission. If Anthropic disappeared tomorrow, MCP would continue to exist. Multiple companies have adopted it - Microsoft (VS Code Copilot), Cursor, Windsurf, JetBrains, and others. This multi-vendor adoption means building an MCP server gives you compatibility with a wide range of AI tools.

OpenAI Plugins are proprietary: Only ChatGPT supports them. OpenAI controls the plugin store, can reject or remove plugins, and can change the specification without community input. The ecosystem is entirely dependent on OpenAI's platform decisions. When OpenAI pivoted from "Plugins" to "GPT Actions," many plugin developers had to rebuild.

For developers building tools, this difference is critical. An MCP server you build today works with Claude Desktop, Cursor, VS Code, and any future MCP-compatible client. An OpenAI plugin only works with ChatGPT.

Local vs Cloud Execution

MCP runs locally by default. When you configure an MCP server in Claude Desktop or Cursor, the server process runs on your machine. It communicates with the AI client over stdio (standard input/output) - data never leaves your computer. This means:

• MCP servers can access local files, databases, and services
• No internet connection required for local tools
• Your data stays on your machine
• No API rate limits or usage costs for the server itself

OpenAI Plugins run in the cloud. ChatGPT sends API requests to your plugin's cloud endpoint. Your plugin needs to be deployed, publicly accessible, and handle authentication. This means:

• Plugins can't access local files or services
• All data passes through OpenAI's infrastructure
• You need hosting and deployment infrastructure
• Plugin endpoints must handle authentication and rate limiting

MCP also supports remote servers via Streamable HTTP transport, giving you the cloud deployment option when needed. But the local-first approach is what makes MCP uniquely powerful for development workflows.

Multi-Client vs Single-Client

MCP servers work across 15+ AI clients: Claude Desktop, Cursor, VS Code (Copilot), Windsurf, Cline, Continue, Zed, JetBrains IDEs, and more. Build once, use everywhere.

OpenAI Plugins/GPT Actions work only in ChatGPT. If you want the same tool in another AI assistant, you need to build a separate integration using that assistant's proprietary format.

This is perhaps MCP's strongest advantage. As a developer, building one MCP server gives your users access in whatever AI tool they prefer. As a user, your MCP servers follow you if you switch from Claude Desktop to Cursor to VS Code.

Developer Experience

Building an MCP Server

MCP servers are straightforward to build. Here's a minimal example:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-tool",
  version: "1.0.0",
});

server.tool(
  "greet",
  "Greet someone by name",
  { name: z.string() },
  async ({ name }) => ({
    content: [{ type: "text", text: `Hello, ${name}!` }],
  })
);

No deployment, no authentication, no OpenAPI spec. Just define tools and run locally. The server is a Node.js script that communicates over stdio. Users install it via npx.

Building an OpenAI Plugin

OpenAI Plugins require more infrastructure:

1. Write an OpenAPI specification for your API
2. Build and deploy a REST API server
3. Create an ai-plugin.json manifest
4. Host it publicly with HTTPS
5. Implement OAuth2 authentication
6. Submit for OpenAI review

The barrier to entry is significantly higher. You need hosting, a domain, SSL certificates, and familiarity with OpenAPI specifications.

Capability Comparison

MCP offers a richer set of capabilities beyond just tools:

• Tools (both support): Functions the AI can call to perform actions
• Resources (MCP only): Expose data that the AI can read, like files, database tables, or configuration
• Prompts (MCP only): Provide prompt templates that users can invoke, like "/summarize-pr" or "/code-review"
• Sampling (MCP only): Allow the server to request AI completions, enabling agent-like behavior within the server itself

OpenAI Plugins are limited to API actions - the equivalent of MCP's tools. They don't have a concept of exposable resources or prompt templates.

When to Use Each

Choose MCP when:

• You want your tool to work with multiple AI clients (not just ChatGPT)
• You need access to local files, databases, or services
• Data privacy is important - data should stay on your machine
• You want to build and iterate quickly without deployment overhead
• You're building developer tools or IDE integrations
• You want to avoid vendor lock-in

Choose OpenAI Plugins/GPT Actions when:

• Your target users only use ChatGPT
• You already have a REST API and want to expose it to ChatGPT
• You want distribution through the GPT Store
• Your tool is cloud-native and doesn't need local access
• You're building for non-technical users who won't configure local servers

Migration Guide: OpenAI Plugin to MCP Server

If you have an existing OpenAI Plugin and want to add MCP support, here is a step-by-step migration path. You do not need to replace your plugin - you can maintain both simultaneously.

Step 1: Identify Your API Endpoints

Your OpenAI Plugin already has an OpenAPI spec listing all endpoints. Each endpoint becomes an MCP tool. For example, if your plugin has a GET /search endpoint, it becomes a search tool in MCP.

Step 2: Create the MCP Server

Build a lightweight MCP server that calls the same API your plugin uses:

// mcp-wrapper.ts - wraps your existing API as MCP tools
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-service",
  version: "1.0.0",
});

// Each OpenAI Plugin endpoint becomes an MCP tool
server.tool(
  "search",
  "Search for items matching a query",
  { query: z.string(), limit: z.number().optional() },
  async ({ query, limit = 10 }) => {
    // Call your existing API (same one the plugin uses)
    const response = await fetch(
      `https://api.myservice.com/search?q=${encodeURIComponent(query)}&limit=${limit}`,
      { headers: { "Authorization": `Bearer ${process.env.API_KEY}` } }
    );
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

Step 3: Publish and Distribute

Publish your MCP server as an npm package. Users install it with npx -y your-mcp-server in their MCP config. No cloud deployment, no OAuth2, no OpenAPI spec needed. The server runs locally and calls your API directly.

Ecosystem Size Comparison

The MCP and OpenAI Plugin ecosystems have grown along different trajectories:

Metric	MCP Ecosystem	OpenAI GPT Store
Total integrations	5,000+ MCP servers	3,000+ GPTs with actions
Growth rate	~500 new servers/month	Slowing (GPTs replacing plugins)
Official/first-party	~30 official servers by Anthropic	~10 first-party integrations
Compatible clients	15+ (Claude, Cursor, VS Code, etc.)	1 (ChatGPT only)
Barrier to publish	None (npm publish or GitHub)	OpenAI review process
Categories covered	Dev tools, databases, APIs, files, search, communication	Productivity, research, creative, utility

MCP's open, permissionless model means new servers appear much faster than GPT Store entries. A developer can build and publish an MCP server in an afternoon. The OpenAI GPT Store requires review, cloud hosting, and OpenAPI specifications, which slows down the publication process.

Can You Use Both?

Yes. If you have an existing REST API, you can expose it both ways:

• Wrap it as an OpenAI Plugin using an OpenAPI spec for ChatGPT users
• Build an MCP server that calls the same API for Claude/Cursor/VS Code users

Many SaaS companies are doing exactly this - maintaining both an OpenAI plugin and an MCP server that connect to the same backend API. The MCP server is often simpler to build since it does not need the OpenAPI specification or OAuth2 flow.

For a comparison of MCP with REST APIs more broadly, see our MCP vs REST APIs guide. Browse all available MCP servers in our server directory, or see which clients support MCP.

The Future of AI Tool Integration

The trend is clearly toward open protocols. Microsoft's adoption of MCP in VS Code Copilot was a major signal - even competitors to Anthropic are choosing MCP over building their own proprietary systems. Google has also expressed interest in MCP compatibility.

OpenAI has responded by evolving their plugin system (rebranding to GPT Actions, adding the Responses API with tool use), but they haven't adopted MCP. Whether they eventually will - or whether MCP and OpenAI's approach coexist indefinitely - remains to be seen.

For developers making a choice today: building MCP gives you the widest reach across AI clients, the best developer experience, and protection against vendor lock-in. Building OpenAI plugins gives you access to ChatGPT's massive user base. Many teams are choosing to support both.