Have you ever wondered what MCP really is and why it’s causing such a stir in the AI world? Let’s chat about the Model Context Protocol (MCP)—what it means, why Anthropic created it, how it works (especially with Claude), what life was like before MCP, and how ChatGPT fits into this evolving landscape.
What Exactly is the Model Context Protocol (MCP)?
At its core, MCP is an open‑source standard designed by Anthropic in November 2024 to let large language models (LLMs) like Claude, and potentially ChatGPT, connect seamlessly to external tools, services, and databases (wikipedia.org). Think of MCP like a “USB‑C port for AI”—a universal, consistent connector that lets any AI assistant plug in to any data source without complex custom integrations.
Instead of the old world where each LLM had its own connector, MCP offers a single client–server architecture:
- MCP host/client (e.g. Claude Desktop or ChatGPT) acknowledges and invokes context.
- MCP server exposes resources, data, or helper functions.
- Communication over JSON‑RPC 2.0 with standardized messages.
“MCP is like the USB‑C port for AI—one connector that plugs in to any data source.”
Why Did Anthropic Create the MCP Protocol?
Everything before MCP was siloed. Building connectors for tools was like building an entirely new bridge for every application—time-consuming, inefficient, and error-prone (anthropic.com).
Anthropic recognized that AI assistants are only as useful as the data they can access. With MCP, they wanted to solve the N×M integration problem: one connector per model per data source—unsustainable in a world of dozens of models and hundreds of data sources.
- Tool and context discovery, all via protocol—no need to custom code.
- Stronger, scalable architectures for enterprise-grade AI workflows (IDEs, automation, CRMs).
- A shared standard, increasing interoperability across ecosystem players.
How MCP Works – A Deep Dive
Architecture Overview
MCP employs a client–server pattern:
- Discovery — MCP host/client learns what resources the server offers (files, DBs, APIs).
- Negotiation & Capabilities — Both client and server agree on JSON‑RPC capabilities.
- Invocation — The LLM (e.g., Claude, ChatGPT) sends requests to perform actions or fetch context (e.g., “read file report.txt”).
- Execution and Response — The server fulfills the request, returns structured data, and may even stream logs or partial results.
- Orchestration — The AI can chain tools dynamically, building multi-step workflows continuity.
MCP Hosts vs MCP Servers
- MCP host/client refers to the AI app (Claude, ChatGPT desktop, IDE plugin).
- MCP server is the external data service (e.g., Google Drive, GitHub, SQL, Puppeteer).
As of March–April 2025, official reference servers include Google Drive, Slack, GitHub, SQL, Puppeteer, Stripe, and more.
Demo with Claude
Using Claude Desktop, Anthropic demoed MCP integrating with GitHub: in under an hour, Claude could create a repository, edit files, generate a pull request—all via MCP.
What Was It Like Before MCP?
Before anything like MCP, AI access to live data or tools required custom connectors or vendor-specific plug‑ins like ChatGPT Plugins, or prompt-based function-calling in OpenAI APIs.
- Integration paralysis: Every API or data source needed its own bespoke code.
- Vendor-lock: Plugins work only in one ecosystem; tough for multi-model flexibility.
- Maintenance burden: Updates in APIs broke connectors.
MCP simplifies this: one single universal pipeline, instead of dozens of per-model integrations.
How It’s Used With Claude
Anthropic baked MCP servers into Claude Desktop. As a host, Claude uses MCP to:
- Read/write files locally.
- Query GitHub repos.
- Fetch documents from Google Drive or Slack.
- Drive Puppeteer for web automation.
When automated workflows need Claude to “look up data, analyze it, then respond,” MCP enables seamless orchestration—and since it’s open standard, companies like Replit, Sourcegraph, Zed, and Block adopted it too. You get dynamic, context-aware Claude agents without reinventing each integration.
And What About ChatGPT?
OpenAI officially adopted MCP in March 2025, integrating it across:
- ChatGPT Desktop (macOS & Windows),
- Agents SDK,
- Responses API (en.wikipedia.org).
This means ChatGPT is now MCP‑enabled—developers can connect ChatGPT to any MCP server, enabling data workflows previously exclusive to other assistants.
However, wide deployment is still rolling out. Be sure to check OpenAI docs—they’re gradually exposing MCP interfaces to developers.
Is MCP Actually Deployed? How to Use It
Current Adoption
- Anthropic: Claude Desktop supports MCP servers locally.
- OpenAI: ChatGPT & SDK are rolling MCP support via their Agents SDK.
- Microsoft integrated MCP support in Windows AI Foundry, including registry and secure prompts.
- Google DeepMind’s Gemini announced planned support in 2025.
Quickstart: MCP Server & Client
Anthropic offers excellent guides:
- MCP Server quickstart: set up a Node.js or Python server, expose endpoints to file/db/APIs.
- MCP Client quickstart: use SDK in your LLM host to discover, authorize, ask, receive.
- User-facing: then in Claude or ChatGPT UI, you’ll see MCP‑enabled tools as commands available.
👨💻 Tip: leverage reference servers (GitHub, Postgres, etc.), then build/customize your own for legacy or proprietary systems.
MCP’s Impact on AI Automation Workflows
Why is MCP a game-changer for downstream automation?
- Cross-model flexibility: Build once, work with Claude, ChatGPT, Gemini…
- Chain-of-thought orchestration: Easily stitch steps like “gather data, analyze, write summary” via MCP.
- Security-controls & consent: Local servers + protocol helps contain scopes; Windows AI Foundry prompts user approvals before granting access.
- Ecosystem scalability: As MCP grows, new servers get added—no need to reinvent connectors.
Security – Benefits & Risks
Every new integration risks misuse. MCP enables potent workflows but also entangles:
- Prompt injection
- Tool poisoning
- Credential leakage
Recent academic research (2025) highlights MCP server vulnerabilities. Strategies to mitigate include:
- Permission boundaries,
- User consent dialogs,
- Registry vetting (like Windows),
- Enterprise-level audit pipelines.
Enterprises handling sensitive data must implement strict governance around MCP.
Key Takeaways and Why You Should Care
- MCP = universal tool interface for Claude, ChatGPT, and beyond.
- Pre‑MCP was siloed; now it’s streamlined.
- Security isn’t baked in—requires layered mitigation.
- Automation workflows level up: natural, context-rich chains across tools.
Call to Action
Curious to test MCP in your AI automations?
- Start local with Claude Desktop + reference servers.
- Explore the OpenAI Agents SDK—add MCP to your next GPT project.
- Evaluate server safety—check emerging tools like MCPSafetyScanner from academic research.
- Chime in: Are you using MCP in your workflow? Share your wins or concerns in the comments!
Conclusion
MCP redefines how AI assistants access and use external data—not a feature, but a new paradigm. It’s bridging models with tools, enabling multi-step workflows, and pushing AI into enterprise-grade automation.
Now, it’s up to professionals like you to design workflows that take full advantage—without compromising security. Ready to plug in?
Let me know which section you’d like to expand next, or if you’d enjoy a hands-on MCP mini-tutorial with code examples!
