Why Anthropic Is Leading AI Development
Background
Recently, Anthropic has released a series of groundbreaking products—Claude Skills, collaborative workflows, and more. In my view, Anthropic has clearly established itself as the leader in AI development.
But what makes their approach so compelling? Let me share my perspective.
A Practical Realization
While working on a project to develop API testing using LLMs, I came to an important realization. Before diving into any AI-powered solution, it's essential to understand a few principles:
1.Don't rely 100% on LLMs. Even before the generative AI era, automation testers were already effective at writing scripts to generate test cases efficiently. AI should augment—not replace—proven methods.
2.Use the right tool for the job. For structured files with fixed syntax (e.g., YAML files for Swagger/OpenAPI), information can be extracted programmatically. There's no need to pass everything through an LLM.
3.Understand where LLMs truly excel. The real strength of LLMs lies in Natural Language Processing (NLP)—interpreting, generating, and reasoning about unstructured text. Use them where this matters.
This mindset—knowing when to use AI and when not to—is fundamental to understanding Anthropic's approach.
MCP: Model Context Protocol
Model Context Protocol (MCP) is a new standard proposed by Anthropic that fundamentally changes how AI interacts with the external world.
The Old Way (Without MCP)
Consider these common workflows:
- Copy code from your project → paste into ChatGPT → ask it to complete a function → copy the result back.
- Receive an email → copy it into ChatGPT → ask for a polite reply → paste the response back into your email client.
You are the hands of the AI—manually shuttling information back and forth.
The New Way (With MCP)
Now imagine this:
- AI directly accesses your project files, identifies what needs to change, and modifies the code—after you grant permission.
- AI reads your email inbox, drafts a reply, and sends it—with you simply clicking "approve."
You go from being the "hands" of the AI to being its supervisor.
Your role shifts from manual labor (copy-paste, execute instructions) to oversight (grant permissions, review results, approve actions).
MCP is the port that connects AI to third-party applications—GitHub, Jira, Gmail, and beyond.
Claude Skills: Intelligence with Context
Understanding MCP makes the next concept intuitive: Claude Skills.
Claude Skills combine two key ideas:
- Boundary awareness — Knowing when to use AI versus traditional methods.
- Direct integration — Leveraging MCP to let AI interact with external tools.
How It Works
A Claude Skill consists of a pre-built SKILL.md file that provides:
- Context: Instructions that tell the AI what this skill is for.
- Scripts: Executable actions to accomplish specific tasks.
Why It Matters
Claude Skills lower the barrier for people without prompt engineering experience. The prompt is already defined—AI decides when and how to use it. You don't need to be an expert to benefit from expert-level workflows.
Summary
In one sentence:
MCP is the connection layer; Claude Skills are the intelligence layer.
- MCP provides standardized access to external applications.
- Claude Skills provide predefined instructions for solving specific problems.
Just as the development of computing standards (TCP/IP, HTTP, APIs) accelerated software innovation, these AI standards are accelerating the evolution of intelligent systems. A maturing standard and a lower barrier to entry drive progress forward.
What's Next?
Coming up: How AI is transforming the medical and pharmaceutical industries.