Exploring Anthropic Skills vs. MCP Servers: First Impressions and Practical Trade-offs
Alternatively titled: ‘Token Efficiency, Customization, and the Realities of LLM Workflows’
Hello fellow datanistas!
Ever wondered how Anthropic’s new ‘skills’ stack up against the more established MCP server pattern for LLM integrations? I spent some time digging through Anthropic’s skills repository, and I wanted to share my first impressions—warts, questions, and all.
This post is a technical walkthrough of what Anthropic’s skills repository actually offers, how it compares to MCP servers, and why token efficiency is such a big deal. If you’re building with LLMs or just curious about the evolving landscape, I hope this helps clarify the trade-offs and open questions.
I started by poking around the skills repo, looking for patterns and practical value. Anthropic’s skills are essentially turnkey bundles for creative workflows (think generative art, themed design outputs, Slack GIFs), document handling (serious support for pptx, docx, pdf, xlsx), and dev utilities (artifact builders, UI testing, MCP server guidance). There’s also a clear structure for making your own skills—folders, SKILL.md files, optional scripts, and assets.
What stood out: skills are designed for minimal prompt footprint. You pass a short description up front, and the model only fetches the heavy details if it decides it needs them. This is a sharp contrast to MCP servers, where tool names and descriptions are sent on every call—great for discoverability, but expensive in tokens.
The trade-offs are real. MCP servers are great for standardization, versioning, and sharing across teams. Skills, on the other hand, are local-first and super customizable, but lack a central registry or easy update path. Token efficiency is the big win for skills, but it comes at the cost of discoverability and standardization.
I’m left with a few open questions: How will teams distribute and update skills at scale? Will skills ever be cross-vendor, or stay Anthropic-only? And what’s the best way to break down complex skills into smaller, composable units?
If you want to see the source and examples, check out the repo: Anthropic Skills Repository. For my full write-up, including more details and open questions, head over to the blog: Exploring Skills vs. MCP Servers.
Skills are a pragmatic attempt to lower token costs and streamline workflows, but MCP servers remain the safer, more standardized choice for now. The right tool depends on your need for customization versus shareability.
How are you thinking about token efficiency and tool integration in your own LLM workflows? Have you tried Anthropic’s skills or stuck with MCP servers? I’d love to hear your experiences or questions.
If this topic resonates, check out the full post for more details and examples: Exploring Skills vs. MCP Servers. Feel free to share your thoughts or subscribe for future deep dives.
Cheers,
Eric