While LLMs have made huge leaps in natural language processing, their ability to interact with live data, apps, and workflows has been limited and inconsistent.

The ecosystem of integrations is fragmented, full of redundant work, and often locked into specific model vendors. MCP is a new protocol that changes this—by offering a unified way to bridge models with real-world systems.

Whether you're building AI products or just trying to connect models to your internal tools, MCP is worth understanding.

The Problem: Isolation by Default

LLMs like ChatGPT, Claude, Gemini, and LLaMA can reason, write, and analyze at a high level. But by default, they operate in isolation from the systems and data that users and businesses actually care about.

This creates two categories of pain:

  • For users: interacting with an LLM means manually copy-pasting data into a chat window, often jumping between apps to gather input or apply output. It’s inefficient and error-prone.
  • For developers: each integration between a language model and an external tool requires custom logic. If you're supporting multiple LLMs and multiple tools, the complexity quickly becomes unmanageable.
    This is known as the NxM problem—N models × M tools = exponential integration effort.

What MCP Does

The Model Context Protocol (MCP) provides a standardized, open way for LLMs to interface with external APIs, databases, and applications. Released by Anthropic as an open-source protocol, MCP builds on existing function-calling mechanisms but abstracts away model-specific quirks.

In short, MCP turns fragmented integration into a plug-and-play architecture. A single interface replaces the need to reinvent the wheel for every model-tool combination.

If you’re looking for ready-to-use tools, the community maintains a public registry of MCP-compatible servers and clients. It’s a great place to explore existing integrations or publish your own. Check out mcp.so for more details.

OpenAI Is Now On Board

In a significant milestone for the protocol’s adoption, OpenAI recently announced official support for MCP within its ecosystem. This means tools built for GPTs—such as GPT Actions—can now interact with external services using the same standardized format as other MCP-compatible apps.

This move signals a major step toward unifying how LLMs access external tools. It also means developers can begin building integrations that work across both OpenAI models and Anthropic’s Claude, using a single abstraction layer.

With both major players now on board, MCP is positioned to become a de facto standard for tooling in the LLM space.

Before vs. After MCP

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Before MCP:

  • Developers define custom schemas and handlers for each LLM/tool combo.
  • Every new model or tool multiplies the integration effort.
  • Maintenance becomes fragile due to API version changes or deprecations.

After MCP:

  • Tools expose their capabilities via a standard MCP server.
  • LLMs (via MCP clients) can discover and use those capabilities through a unified API.
  • The system becomes modular, easier to scale, and easier to maintain.

Architecture Overview

MCP follows a client-server architecture inspired by the Language Server Protocol (LSP). The main components are:

  • Host application: The LLM-enabled app (e.g. Claude Desktop, a web chat UI, an IDE plugin).
  • MCP client: Embedded in the host app; handles communication with MCP servers.
  • MCP server: Exposes tools and capabilities to the client (e.g. access to Slack, a database, Stripe, etc).
  • Transport layer: STDIO for local, HTTP+SSE for remote communication. All exchanges use JSON-RPC 2.0.

How a Request Works

  1. The user makes a request (e.g. “What’s the status of project X in GitHub?”).
  2. The LLM recognizes that it needs external context.
  3. The MCP client identifies an appropriate tool from the registered MCP servers.
  4. The user is prompted for permission.
  5. If approved, the client sends a standardized request to the tool.
  6. The tool responds with structured data.
  7. The LLM integrates the result and generates a complete response.

This all happens in real time, with a clean separation between AI reasoning and data/tool execution.

Real-World Examples

MCP is already being adopted across various platforms:

  • MCP clients: Claude Desktop, IDEs like Cursor and Continue, Sourcegraph Cody, and open-source tools like LangChain or Firebase Genkit.
  • MCP servers: GitHub (repos), PostgreSQL (read-only queries), Slack (channels, messages), Stripe (invoices), JetBrains (code navigation), Apify (scraping), Discord, Docker, HubSpot, and more.

Many of these tools are listed at mcp.so, making it easier for developers to discover and contribute to the growing ecosystem.

Security Considerations

MCP uses OAuth for secure authorization in remote server scenarios. Clients must prompt users for approval before accessing any tools. Server developers are encouraged to:

  • Follow the principle of least privilege.
  • Avoid open redirects.
  • Implement PKCE and token hygiene.
  • Use clear and minimal scopes.

The human-in-the-loop design adds a layer of defense against unauthorized access.

What’s Ahead

Some features under development:

  • MCP registry: A centralized directory for discovering servers and tools.
  • Sampling: Letting MCP servers request LLM completions (AI-to-AI collaboration).
  • Authorization hardening: Evolving standards for secure deployment.

Final Thoughts

MCP represents a major shift in how LLMs interact with the world. Instead of building custom integrations for each use case, developers can plug into a standardized ecosystem that’s open, modular, and designed for interoperability.

Now that OpenAI has joined Anthropic in supporting MCP, the protocol is poised to become the connective tissue for intelligent applications across the AI landscape.

The age of isolated models is ending—and that’s a good thing. And no, we’re not building Skynet. But we wouldn’t blame Sarah Connor for keeping an eye on it anyway.