Anthropic MCP vs OpenAI Plugins vs Google Extensions: AI Integration Compared

AI assistants are only as useful as the tools they can access. In 2025, three major AI labs — Anthropic, OpenAI, and Google — have each built their own integration frameworks for connecting large language models to external data, APIs, and services. But they’ve taken remarkably different approaches.

This comparison breaks down Anthropic MCP, OpenAI Plugins/Function Calling, and Google Gemini Extensions across architecture, use cases, developer experience, and practical performance.

Understanding the Three Frameworks

Anthropic Model Context Protocol (MCP)

MCP is an open protocol released by Anthropic in late 2024. Rather than a plugin marketplace, MCP is a standardized communication layer — a universal interface between AI models and external tools or data sources. Think of it as USB-C for AI integrations.

MCP allows developers to build “servers” that expose tools, resources, and prompts to any compatible AI client. Claude desktop, Claude API users, and third-party AI applications can all connect to MCP servers.

Core components:

• MCP Servers: Local or remote processes that expose capabilities
• Resources: Structured data the AI can read (files, databases, APIs)
• Tools: Actions the AI can invoke (run code, query APIs, write files)
• Prompts: Templated instructions that guide AI behavior
• Transport: stdio (local) or HTTP with SSE (remote)

A key design choice: MCP is open and model-agnostic. Though developed by Anthropic for Claude, the protocol is publicly documented and can theoretically support any LLM.

OpenAI Plugins and Function Calling

OpenAI pioneered LLM tool use with the ChatGPT Plugin ecosystem (launched 2023), then evolved toward a more robust function calling API, and most recently introduced the Assistants API with Tools — which includes code interpreter, file search, and custom function definitions.

OpenAI’s approach has evolved significantly:

• ChatGPT Plugins (2023–2024): A plugin marketplace where users could install third-party capabilities. Officially deprecated in early 2024.
• Function Calling API: Developers define functions in JSON schema; the model decides when to call them and structures outputs accordingly.
• Assistants API + Tools: Persistent assistant instances with built-in code interpreter, file search, and custom functions.
• GPT Actions (in GPTs): Custom GPT builders can configure API calls that the assistant invokes automatically.

Google Gemini Extensions

Google Gemini (formerly Bard) uses an “Extensions” system that connects the model to Google’s own services and select third-party partners. In the developer API, Gemini supports function calling similar to OpenAI’s approach, plus native integration with Google Workspace, Maps, Flights, and other Google properties.

• First-party extensions: Google Search, Maps, Flights, Hotels, YouTube, Workspace
• Function calling API: Similar JSON schema approach to OpenAI
• Code execution: Built-in Python code execution tool
• Grounding with Google Search: Real-time search grounding for factual accuracy

Head-to-Head Comparison

Architecture Deep Dive

MCP: The Protocol Approach

MCP’s most distinctive feature is its protocol-first architecture. Instead of defining how one AI calls one API, MCP defines a universal communication standard. An MCP server built for file system access can be used by any MCP-compatible client — today Claude, tomorrow potentially GPT-5 or Gemini 3.

This creates a true separation of concerns: tool developers build MCP servers once; AI model providers implement the client once; integrations multiply combinatorially.

The stdio transport option is particularly notable — it enables fully local, air-gapped AI integrations where sensitive data never leaves your machine. For enterprise and regulated industries, this is a significant security advantage.

OpenAI: The API-First Approach

OpenAI’s function calling is developer-friendly precisely because it requires no infrastructure. You define functions as JSON schemas in your API call, and the model returns structured JSON when it decides to invoke a function. Your application code handles the actual execution.

This simplicity is a major strength for application developers who want tool use without managing separate server processes. The tradeoff is that all data passes through OpenAI’s cloud — a concern for sensitive enterprise data.

The Assistants API adds persistence and built-in tools (code interpreter processes files server-side in OpenAI’s sandbox), trading control for convenience.

Google: The Ecosystem Approach

Google’s extension system is fundamentally different from the others in one key way: it treats Google’s own service ecosystem as the primary integration surface. Need real-time flight data? Google Flights extension. Current stock prices? Google Finance. Maps directions? Done.

For use cases centered on Google services, this is remarkably powerful and requires near-zero developer effort. For custom integrations, Gemini’s function calling API offers similar capability to OpenAI’s, with the added bonus of grounding — allowing the model to search Google in real time to verify its responses.

Developer Experience Comparison

Getting Started with Anthropic MCP

# Install MCP SDK
npm install @modelcontextprotocol/sdk

# Create a simple MCP server
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
const server = new Server({ name: "my-tool-server", version: "1.0.0" });
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{ name: "get_data", description: "Fetch data", inputSchema: {...} }]
}));

MCP requires running a server process, which adds infrastructure overhead but enables powerful local-first architectures.

Getting Started with OpenAI Function Calling

# Simple function calling
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {"type": "object", "properties": {"location": {"type": "string"}}}
        }
    }]
)

No server required — just define schemas in your API call. The simplest path to LLM tool use.

Real-World Use Cases: Which Wins?

Enterprise Data Integration

Winner: Anthropic MCP. The ability to run MCP servers locally means sensitive enterprise data (CRM records, financial data, internal documents) never has to leave your infrastructure. MCP’s resource-first design also maps naturally to enterprise data access patterns.

Rapid Application Development

Winner: OpenAI Function Calling. The JSON schema approach requires no additional infrastructure and integrates directly into any API application. For startups and rapid prototypes, this is the fastest path to functional AI tool use.

Google Workspace Integration

Winner: Google Gemini Extensions. If your workflow revolves around Gmail, Google Calendar, Google Docs, and Google Drive, Gemini’s first-party extensions provide seamless, zero-configuration integration that neither MCP nor OpenAI can match out of the box.

Agentic/Long-Running Workflows

Winner: Anthropic MCP. MCP’s design explicitly supports complex agentic workflows where an AI orchestrates multiple tools in sequence. The protocol-level design scales better for multi-step, long-horizon tasks than ad-hoc function calling.

Factual Accuracy / Real-Time Data

Winner: Google Gemini. Search grounding — where Gemini verifies responses against Google Search in real time — is a significant advantage for information-heavy use cases where accuracy is paramount.

The Future of AI Integration Standards

The most interesting development in 2025 is whether MCP becomes the de facto standard for AI tool integration across models. Several signals suggest this is possible:

• MCP has been endorsed by major IDE vendors (Cursor, VS Code) as their AI tool integration standard
• Dozens of popular MCP servers are already available (GitHub, Slack, PostgreSQL, web scraping, and more)
• OpenAI and Google could theoretically adopt MCP client support, making their models MCP-compatible
• The open, protocol-level design aligns with how the industry has historically standardized (HTTP, OAuth, etc.)

Conversely, OpenAI’s function calling has become so widely adopted in the developer ecosystem that it also has strong standardization momentum. Many AI frameworks (LangChain, CrewAI, AutoGen) speak OpenAI’s function calling format natively.

Choosing the Right Framework for Your Project

Here’s a practical decision guide:

• Building a local AI agent with access to your files/system? → Use MCP with Claude
• Adding tool use to an existing web/mobile app quickly? → Use OpenAI function calling
• Heavy Google Workspace user, need seamless integration? → Use Gemini Extensions
• Enterprise with data privacy requirements? → Use MCP (local servers)
• Building reusable tools across multiple AI systems? → Build MCP servers (future-proof)
• Need real-time factual grounding? → Use Gemini with search grounding

Frequently Asked Questions

Is Anthropic MCP better than OpenAI function calling?

It depends on your use case. MCP is more architecturally flexible and privacy-preserving (especially for local deployments), while OpenAI function calling is simpler to implement for API-based applications. For complex agentic workflows and enterprise deployments, MCP has significant advantages.

Can I use MCP with GPT-4 or Gemini?

MCP is open and theoretically model-agnostic, but as of early 2025, official client support is primarily in Claude (Desktop and API). Community projects exist to bridge MCP to other models, and broader adoption is expected as the standard matures.

Are ChatGPT Plugins still available?

ChatGPT Plugins were officially deprecated by OpenAI in early 2024. The successor is the GPT Actions system (for custom GPTs) and the Assistants API with Tools for developers.

Which AI integration framework has the largest ecosystem?

OpenAI’s function calling has the largest existing ecosystem by number of integrations, given its earlier introduction and wide adoption. However, the MCP ecosystem is growing extremely rapidly — with hundreds of servers published in the first six months after launch.

Is Google Gemini’s function calling API compatible with OpenAI’s format?

Gemini’s function calling API is similar in concept but uses a different format and SDK. Several abstraction libraries (like LiteLLM) provide compatibility layers, but direct cross-compatibility requires adaptation code.