MCP vs Traditional AI Plugins: Why MCP is the Future

December 15, 2024 · 8 min read

ToolBoost Engineering Team

If you've used ChatGPT plugins, Claude Code extensions, or other AI integrations, you might wonder: "How is MCP different?"

In this post, we'll compare MCP to traditional AI plugin architectures and explain why MCP represents a fundamental shift in how AI assistants connect to external tools.

The Evolution of AI Integrations

Phase 1: Hardcoded Integrations (2020-2022)

Early AI assistants had built-in, hardcoded integrations:

AI Assistant → [Built-in Code Execution]
AI Assistant → [Built-in Web Search]
AI Assistant → [Built-in Calculator]

Limitations:

Limited to what the vendor built
No customization possible
Updates required vendor involvement
Couldn't integrate with your systems

Phase 2: Plugin Systems (2022-2023)

ChatGPT Plugins, Claude Code extensions, and similar systems emerged:

AI Assistant → Plugin 1 (Zapier)
AI Assistant → Plugin 2 (Wolfram)
AI Assistant → Plugin 3 (Web Search)

Improvements:

Third-party developers could create integrations
More tools available
Some customization possible

Still Limited:

Platform-specific (ChatGPT plugins ≠ Claude plugins)
Controlled by platform vendor
Separate implementations needed for each AI
Vendor lock-in

Phase 3: Model Context Protocol (2024+)

MCP introduces a universal standard:

AI Client 1 (Claude) ┐
AI Client 2 (Cursor) ├─→ MCP Protocol → MCP Server (GitHub)
AI Client 3 (Custom) ┘

Revolution:

One server works with all MCP-compatible clients
Open standard, not controlled by any vendor
Complete flexibility
No lock-in

Deep Dive: MCP vs ChatGPT Plugins

Let's compare MCP to the most popular plugin system: ChatGPT Plugins.

Architecture

ChatGPT Plugins:

ChatGPT → OpenAPI Spec → Your API Endpoint

You provide:

OpenAPI specification (JSON)
HTTP API endpoints
OAuth configuration

MCP:

AI Client → MCP Protocol (JSON-RPC) → MCP Server

You provide:

MCP server implementation
Tools, resources, and prompts
Transport layer (stdio, HTTP/SSE)

Comparison Table

Feature	ChatGPT Plugins	MCP
Platform Lock-in	OpenAI only	Any MCP client
Protocol	OpenAPI/REST	JSON-RPC over stdio/HTTP
Authentication	OAuth 2.0	Flexible (API keys, OAuth, etc.)
Data Types	JSON only	Text, images, binary
Streaming	Limited	Native SSE support
Local Execution	No (cloud only)	Yes (stdio transport)
Resources	Not supported	First-class concept
Prompts	Not supported	First-class concept
Stateful	Stateless HTTP	Can be stateful
Privacy	Data sent to OpenAI	Can be fully local
Cost	ChatGPT Plus required	Free (with compatible client)
Discovery	OpenAI Plugin Store	Open ecosystem
Approval Process	OpenAI review required	No approval needed

Code Comparison

ChatGPT Plugin (OpenAPI manifest):

openapi: 3.0.0
info:
  title: Task Manager
  version: 1.0.0
paths:
  /tasks:
    get:
      summary: List all tasks
      operationId: listTasks
      responses:
        '200':
          description: Success
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/Task'
    post:
      summary: Create a task
      operationId: createTask
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateTaskRequest'

MCP Server (TypeScript):

server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [
      {
        name: 'list_tasks',
        description: 'List all tasks',
        inputSchema: {
          type: 'object',
          properties: {
            status: { type: 'string', enum: ['todo', 'done'] }
          }
        }
      },
      {
        name: 'create_task',
        description: 'Create a new task',
        inputSchema: {
          type: 'object',
          properties: {
            title: { type: 'string' },
            description: { type: 'string' }
          },
          required: ['title']
        }
      }
    ]
  };
});

Key Differences

1. Universal Compatibility

ChatGPT Plugins:

Only work with ChatGPT
Need separate implementations for Claude, Gemini, etc.

MCP:

One server works with Claude, Cursor, Windsurf, Zed, and any future MCP client
Write once, use everywhere

2. Resources (Unique to MCP)

MCP has a concept of "resources" - read-only data sources:

// MCP: AI can browse available data
server.setRequestHandler(ListResourcesRequestSchema, async () => {
  return {
    resources: [
      { uri: 'file:///project/README.md', name: 'Project README' },
      { uri: 'file:///project/src/main.ts', name: 'Main Source' }
    ]
  };
});

ChatGPT plugins have no equivalent - you'd need to create a separate API endpoint for each file.

3. Prompts (Unique to MCP)

MCP supports reusable prompt templates:

server.setRequestHandler(GetPromptRequestSchema, async (request) => {
  if (request.params.name === 'code_review') {
    return {
      messages: [{
        role: 'user',
        content: {
          type: 'text',
          text: 'Review this pull request and suggest improvements:\n\n' + prContent
        }
      }]
    };
  }
});

Plugins can't provide context or prompt templates to the AI.

4. Local Execution

ChatGPT Plugins:

Must be hosted on public internet
OpenAI servers connect to your API
No local/private deployments

MCP:

Can run entirely local via stdio transport
No internet connection required
Perfect for sensitive data

{
  "mcpServers": {
    "local_files": {
      "command": "node",
      "args": ["local-mcp-server.js"]
    }
  }
}

5. Approval Process

ChatGPT Plugins:

Build plugin
Submit to OpenAI
Wait for review (weeks)
Hope for approval
Listed in ChatGPT Plugin Store

MCP:

Build MCP server
Deploy anywhere (or run locally)
Use immediately
Share with anyone
Optional: List on community catalogs

MCP vs Claude Code Extensions

Claude Code (the VS Code extension) also has its own extension system. How does MCP compare?

Claude Code Extensions

Limited to:

VS Code environment
Extensions written in TypeScript/JavaScript
Must be installed as VS Code extensions

Example:

// VS Code extension API
export function activate(context: vscode.ExtensionContext) {
  let disposable = vscode.commands.registerCommand('extension.doSomething', () => {
    vscode.window.showInformationMessage('Hello!');
  });

  context.subscriptions.push(disposable);
}

MCP Advantage

Works with:

Claude Desktop (standalone app)
Claude Code (VS Code)
Cursor
Windsurf
Zed
Any future MCP client

Example:

// MCP server works everywhere
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name === 'do_something') {
    return {
      content: [{ type: 'text', text: 'Hello from MCP!' }]
    };
  }
});

MCP vs Langchain Tools

Langchain has its own tool system for AI agents. How does it compare?

Langchain Tools

Characteristics:

Python/JavaScript library
Code-based tool definitions
Embedded in your application
Agent-specific

Example (Python):

from langchain.tools import BaseTool

class CalculatorTool(BaseTool):
    name = "calculator"
    description = "Useful for math calculations"

    def _run(self, query: str) -> str:
        return str(eval(query))  # Don't actually do this!

MCP Advantage

Characteristics:

Protocol, not library
Language-agnostic server
Standalone process
Client-agnostic

Why this matters:

Aspect	Langchain	MCP
Reusability	Tied to your app	Universal
Language	Python or JS	Any language
Sharing	Code only	Deployable service
Discovery	Manual coding	Dynamic listing
Updates	Redeploy app	Update server only

Real-World Scenario

Let's say you want to give AI access to your company's CRM system.

With ChatGPT Plugins

Build REST API with OpenAPI spec
Submit to OpenAI for approval
Wait weeks for review
Only works with ChatGPT
Need ChatGPT Plus subscription
Data flows through OpenAI servers
Limited to approved users

Time to production: 4-6 weeks

With Langchain

Write Python tool class
Embed in your application
Deploy your application
Users must use your specific app
Can't use with other AI tools

Time to production: 1-2 weeks

With MCP

Build MCP server (see our tutorial)
Deploy to ToolBoost or run locally
Share connection URL with team
Works with Claude, Cursor, Windsurf, etc.
Data stays private (can be local-only)
No approval needed

Time to production: 1-2 days

ToolBoost hosted:

{
  "mcpServers": {
    "company_crm": {
      "serverUrl": "https://toolboost.dev/server/yourcompany/crm/mcp?api_key=YOUR_KEY"
    }
  }
}

Self-hosted:

{
  "mcpServers": {
    "company_crm": {
      "command": "docker",
      "args": ["run", "yourcompany/crm-mcp-server"]
    }
  }
}

Why MCP is Winning

1. Open Standard

MCP is not controlled by any single company:

Anthropic created it but doesn't control it
Open specification
Community-driven
No vendor lock-in

2. Growing Ecosystem

Clients:

Claude Desktop
Cursor
Windsurf
Zed
Continue.dev
Custom implementations

Servers:

5,000+ on ToolBoost
Hundreds on GitHub
Growing daily

3. Developer Experience

Building an MCP server is straightforward:

// That's it! You have an MCP server
import { Server } from '@modelcontextprotocol/sdk/server/index.js';

const server = new Server({ name: 'my-server', version: '1.0.0' });

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{ name: 'my_tool', description: 'Does something', inputSchema: {} }]
}));

Compare to ChatGPT Plugin:

Write OpenAPI spec
Build REST API
Implement OAuth
Create manifest
Submit for review
Wait...

4. Flexibility

Transport Options:

stdio (local, private)
HTTP with SSE (cloud-hosted)
Custom transports (community)

Deployment Options:

Local process
Docker container
Cloud function
ToolBoost managed

Data Privacy:

Fully local (stdio)
Self-hosted
Cloud with encryption
Hybrid approaches

5. Future-Proof

MCP is designed for the future:

Not tied to any AI model
Supports multimodal (text, images, etc.)
Extensible protocol
Active development

Migration Paths

From ChatGPT Plugins to MCP

If you have a ChatGPT plugin, converting to MCP is straightforward:

// Your existing API endpoint
app.post('/api/tasks', async (req, res) => {
  const task = await createTask(req.body);
  res.json(task);
});

// Becomes MCP tool
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name === 'create_task') {
    const task = await createTask(request.params.arguments);
    return {
      content: [{ type: 'text', text: JSON.stringify(task) }]
    };
  }
});

From Langchain Tools to MCP

# Langchain tool
class MyTool(BaseTool):
    def _run(self, input: str) -> str:
        return do_something(input)

# Becomes MCP server (Python)
from mcp import Server

server = Server("my-tool")

@server.tool()
def my_tool(input: str) -> str:
    return do_something(input)

The Bottom Line

Aspect	Plugins	MCP
Universality	Platform-specific	Universal
Control	Vendor-controlled	Open standard
Privacy	Cloud-dependent	Can be local
Flexibility	Limited	Highly flexible
Speed	Slow approval	Instant deployment
Future	Uncertain	Future-proof

Conclusion

MCP isn't just another plugin system - it's a fundamental rethinking of how AI assistants should integrate with external tools.

Key Advantages:

✅ Universal compatibility
✅ No vendor lock-in
✅ Local execution possible
✅ Rich feature set (tools, resources, prompts)
✅ Fast deployment
✅ Open ecosystem

Traditional plugin systems are fragmented and vendor-controlled. MCP provides a universal, open standard that works everywhere.

The future of AI integrations is here, and it's called MCP.

Ready to build with MCP? Check out our getting started guide.

Want to migrate from plugins to MCP? Contact ToolBoost for assistance.

Running existing plugins? Deploy them to MCP with ToolBoost and get universal compatibility.

The Evolution of AI Integrations​

Phase 1: Hardcoded Integrations (2020-2022)​

Phase 2: Plugin Systems (2022-2023)​

Phase 3: Model Context Protocol (2024+)​

Deep Dive: MCP vs ChatGPT Plugins​

Architecture​

Comparison Table​

Code Comparison​

Key Differences​

1. Universal Compatibility​

2. Resources (Unique to MCP)​

3. Prompts (Unique to MCP)​

4. Local Execution​

5. Approval Process​

MCP vs Claude Code Extensions​

Claude Code Extensions​

MCP Advantage​

MCP vs Langchain Tools​

Langchain Tools​

MCP Advantage​

Real-World Scenario​

With ChatGPT Plugins​

With Langchain​

With MCP​

Why MCP is Winning​

1. Open Standard​

2. Growing Ecosystem​

3. Developer Experience​

4. Flexibility​

5. Future-Proof​

Migration Paths​

From ChatGPT Plugins to MCP​

From Langchain Tools to MCP​

The Bottom Line​

Conclusion​

The Evolution of AI Integrations

Phase 1: Hardcoded Integrations (2020-2022)

Phase 2: Plugin Systems (2022-2023)

Phase 3: Model Context Protocol (2024+)

Deep Dive: MCP vs ChatGPT Plugins

Architecture

Comparison Table

Code Comparison

Key Differences

1. Universal Compatibility

2. Resources (Unique to MCP)

3. Prompts (Unique to MCP)

4. Local Execution

5. Approval Process

MCP vs Claude Code Extensions

Claude Code Extensions

MCP Advantage

MCP vs Langchain Tools

Langchain Tools

MCP Advantage

Real-World Scenario

With ChatGPT Plugins

With Langchain

With MCP

Why MCP is Winning

1. Open Standard

2. Growing Ecosystem

3. Developer Experience

4. Flexibility

5. Future-Proof

Migration Paths

From ChatGPT Plugins to MCP

From Langchain Tools to MCP

The Bottom Line

Conclusion