The world of AI assistants is evolving fast, and a quiet revolution is happening right on your desktop. Both ChatGPT and Claude now support adding connectors powered by the Model Context Protocol (MCP), letting users link their favorite AI directly to custom tools, APIs, or even secure on-prem systems.

This isn’t just a new feature. It’s a major step toward a more open, extensible AI ecosystem, one where your assistant can understand, interact with, and act upon the systems that matter most to you.

What Are MCP Servers?

At its core, MCP (Model Context Protocol) defines a simple, standard way for AI models to connect with external services. An MCP server is any application that exposes a set of tools or data sources the model can call, securely and with full control.

Think of it as a translator between your AI and your world:

  • The model sends structured requests (“run this query,” “fetch this record,” “generate this report”).
  • The MCP server executes them using your own APIs, databases, or apps.
  • Results flow back into the conversation, so the model can reason about them naturally.

For developers, OpenAI’s Apps SDK makes it straightforward to define these servers in Python, TypeScript, or other languages.

Why This Is a Big Deal

Until now, both ChatGPT and Claude operated mostly in self-contained environments. With MCP support, they can now act as frontends for your own services whether that’s:

  • Querying your company’s internal database,
  • Managing cloud infrastructure,
  • Fetching live analytics data,
  • Or controlling IoT systems and software tools.

MCP connectors turn static chatbots into secure, interactive agents. They let you bring your own logic, tools, and security policies to the table, rather than relying solely on what the AI provider hosts.

MCP Connector Architecture

MCP connectors follow a unified architecture pattern that enables AI assistants like ChatGPT and Claude to interact with external tools and services. This architecture is designed to be flexible, secure, and adaptable to different deployment scenarios.

Core Components

  1. AI Assistant Interface: The user-facing application (ChatGPT web/app or Claude Desktop)
  2. Model Provider API: The backend service that handles model inference (OpenAI API or Anthropic API)
  3. MCP Bridge: The component that facilitates communication between the AI assistant and MCP servers
  4. MCP Server: Your custom server implementing the Model Context Protocol
  5. Tool Registry: Where tools are defined, registered, and documented
  6. Authentication Layer: Manages secure connections between all components
  7. External Systems: The actual services, databases, or APIs that tools interact with

Architecture Diagram

flowchart TD
    User[User] <--> Assistant[AI Assistant Interface]
    Assistant <--> ModelAPI[Model Provider API]
    Assistant <--> Bridge[MCP Bridge]
    Bridge <--> Auth[Authentication Layer]
    Auth <--> MCPServer[MCP Server]
    MCPServer <--> Tools[Tool Registry]
    MCPServer <--> ExternalSystems[External Systems]
    
    subgraph "Your Infrastructure"
        MCPServer
        Tools
        ExternalSystems
    end
    
    subgraph "AI Provider Infrastructure"
        Assistant
        ModelAPI
        Bridge
        Auth
    end

    classDef local fill:#e6f7ff,stroke:#0066cc
    classDef remote fill:#fff2e6,stroke:#ff8000
    
    class MCPServer,Tools,ExternalSystems local
    class Assistant,ModelAPI,Bridge,Auth remote

Deployment Options

The MCP architecture supports multiple deployment scenarios:

  1. Cloud-Based: MCP server runs in the cloud with public endpoints
    • Ideal for services that need to be accessible from anywhere
    • Simplifies maintenance and updates
    • Requires proper security measures for public exposure
  2. Local-Only: MCP server runs on the user’s machine
    • Maximizes privacy and data security
    • Eliminates need for public endpoints
    • Ideal for sensitive data or operations
    • Limited to when the local machine is running
  3. Hybrid: Combination of local and remote MCP servers
    • Balances privacy and accessibility
    • Can route sensitive operations locally while keeping general tools in the cloud
    • Provides flexibility for different use cases

Connection Flow

Regardless of the specific AI assistant (ChatGPT or Claude), the connection flow follows these general steps:

  1. Registration: The MCP server is registered with the AI assistant platform
  2. Authentication: Secure credentials are established between components
  3. Discovery: The AI assistant discovers available tools from the MCP server
  4. Authorization: The user grants permission for the AI to use specific tools
  5. Tool Manifest: The MCP server provides schemas and metadata for all tools
  6. Integration: The AI model can now reason about and use these tools during conversations

MCP Connector Workflow

The following diagram illustrates the unified workflow of how AI assistants interact with MCP connectors:

sequenceDiagram
    participant User
    participant Assistant as AI Assistant
    participant Model as Model API
    participant Bridge as MCP Bridge
    participant MCP as MCP Server
    participant External as External Systems
    
    User->>Assistant: Ask question requiring tool use
    Assistant->>Model: Process query
    Model->>Model: Determine tool needed
    Model->>Assistant: Request tool execution
    Assistant->>Bridge: Forward tool request
    Bridge->>MCP: Authenticated tool call
    MCP->>MCP: Validate request
    MCP->>External: Execute operation
    External->>MCP: Return results
    MCP->>Bridge: Format and return results
    Bridge->>Assistant: Return results
    Assistant->>Model: Send results for processing
    Model->>Assistant: Generate complete response
    Assistant->>User: Display response
    
    Note over Assistant,MCP: All communication uses MCP standard

Key Workflow Steps

  1. Query Analysis: The AI model analyzes the user query to determine if tools are needed
  2. Tool Selection: The model selects the appropriate tool based on the query and available tools
  3. Parameter Preparation: The model formats a structured request with the necessary parameters
  4. Authentication & Authorization: The request is authenticated and authorized before execution
  5. Tool Execution: The MCP server processes the request and interacts with external systems
  6. Result Processing: The tool results are formatted according to the MCP standard
  7. Response Integration: The AI model incorporates the tool outputs into its reasoning
  8. User Presentation: The final response, including tool results, is presented to the user

Implementation Variations

While the general workflow is consistent across platforms, there are some implementation differences:

  • ChatGPT typically processes tool calls in the cloud, with results flowing through OpenAI’s infrastructure
  • Claude Desktop can execute some tool calls locally, enhancing privacy for sensitive operations
  • Authentication methods may vary between platforms (OAuth, API keys, local authentication)
  • Tool discovery mechanisms differ (manual registration vs. local network discovery)

These variations allow the same MCP server to work with different AI assistants while optimizing for specific use cases and security requirements.

MCP Implementation Comparison

Feature Cloud-Based Implementation Local Implementation
Hosting Model Remote servers with public endpoints Runs on user’s machine
Authentication OAuth 2.0, API keys Local authentication, API keys
Privacy Data passes through cloud services Local execution keeps data private
Discovery Manual registration through portals Automatic local discovery + manual registration
Deployment Requires public endpoint Can run entirely on local network
Tool Execution Remote execution Local execution
Update Mechanism Server-side updates Client-side updates
Security Boundary Cloud security model Local security boundary

Both ChatGPT and Claude support these implementation models, with ChatGPT typically favoring cloud-based implementations and Claude Desktop offering stronger support for local implementations. However, both platforms are evolving to support the full spectrum of deployment options.

Getting Started with MCP Connectors

Setting up an MCP connector is refreshingly simple:

  1. Create an MCP server using MCP SDK or FastMCP.
  2. Register the connector in ChatGPT or Claude Desktop.
  3. Authorize and connect from there, your model can call your tools just like built-in functions.

For instance, you could create a connector that lets ChatGPT query your project management system, check live metrics from a Kubernetes cluster, or access your company’s internal document search API.

Because MCP defines both the schema and metadata for each tool, models can interpret your connectors intelligently, knowing when and how to use them.

A Quick Example: AI Meets Game Servers

Imagine you run a Minecraft server and want to manage it directly from ChatGPT.

With MCP, you can build a connector that:

  • Reports online players,
  • Changes game settings, or
  • Launches new instances.

ChatGPT then interacts with this server just as it would with its built-in tools, issuing commands, reading structured responses, and explaining results conversationally.

It’s automation, insight, and conversation rolled into one.

Best Practices and Considerations

As powerful as MCP is, good design matters.
Some guidelines to follow:

  • Security first: use token-based or OAuth authentication.
  • Describe tools clearly: models rely on metadata to reason effectively.
  • Handle rate limits and errors gracefully.
  • Version your connectors so the assistant knows what’s changed.

With these best practices, MCP can scale safely from hobby projects to enterprise-grade integrations.

Future Prospects: Expanding Beyond the Cloud

The addition of MCP connector support in ChatGPT and Claude desktops marks the start of a new phase in AI integration, one that goes far beyond cloud-based connectivity. The Model Context Protocol’s open design allows connectors to run anywhere: in the cloud, on edge devices, or even fully on-premises.

For organizations with strict data governance or regulatory requirements, on-prem MCP servers can host proprietary tools and datasets securely behind the firewall. This means enterprises can expose internal APIs, databases, or analytical services to ChatGPT and Claude without sending sensitive data outside their own environment. Models interact only through the standardized MCP interface, while all computation and storage stay local.

Looking forward, we’re likely to see:

  • Hybrid connector architectures where some services run on-prem and others in the cloud.
  • Federated orchestration, letting AI assistants call across multiple connectors to perform multi-step tasks.
  • Enterprise marketplaces for approved MCP connectors with built-in compliance and auditing.
  • Connector discovery and versioning, enabling safer, more dynamic ecosystems.

As this ecosystem matures, MCP could become the universal “bridge layer” between private infrastructure and general-purpose AI assistants, empowering organizations to connect their own world securely to the intelligence of models like ChatGPT and Claude.

Conclusion: A Smarter, More Connected Future

By embracing MCP connectors, both ChatGPT and Claude are evolving from closed conversational tools into AI operating systems, flexible, extensible, and ready to integrate with the real world.

Whether you’re an independent developer experimenting with APIs or an enterprise architect designing secure on-prem solutions, MCP opens the door to a new class of intelligent, connected assistants.

So go ahead, spin up a server, register your connector, and start teaching your AI about your world.
The next era of conversational computing is not just smarter, it’s connected.