Will Claude Skills Replace MCP? In-Depth Analysis and Trend Forecast
The launch of Claude Skills raises an important question: will it replace MCP? This article will provide an in-depth analysis of this question from technical, application, and ecosystem development perspectives.
Claude Skills Core Features Review
Claude Skills can be understood as a sophisticated, efficient “capability plugin” system, with its core enabling Claude models to invoke pre-packaged professional knowledge and tools when executing specific tasks.
Core Advantages
Encapsulation and Modularity
- Skills package instructions, scripts, and even executable code needed for specific tasks into independent “folders” or modules
- Like preparing standardized toolkits for AI
- Unified Agent engineering standards, reduced implementation difficulty
- In Claude’s context, an Agent can consist of multiple Skills
Efficiency
- System doesn’t blindly load all capabilities
- Automatically scans and only invokes the most relevant Skill when processing tasks
- Only loads minimum needed information and files, ensuring speed and efficiency
Definitive Context Engineering Paradigm
- Through SKILL, FORMS, REFERENCE, scripts and bound remote VM environment, forms a complete context
- Complete lifecycle from definition, transmission to execution
- According to reliable sources, Claude Skill implementation deeply draws from Manus design philosophy
Composable and Stackable
- Multiple Skills can work together
- Claude can automatically identify and coordinate to complete more complex tasks
- Embodies “LEGO-style” capability combination
Reliability and More Permissions
- Breakthrough feature: Skills can contain executable code
- For tasks where traditional programming is more reliable than LLM generation (precise calculations, complex data processing)
- Skills can ensure accuracy and stability through code execution
- Transcends pure Token generation
Portability and Cross-Platform
- Skills adopt unified format
- Build once, use seamlessly across Claude’s various applications (web, code assistant Claude Code) and API
- Greatly improves development and deployment efficiency
MCP: The “USB-C” Connecting LLMs and Applications
MCP can be thought of as the USB-C interface for AI applications. Just as USB-C provides standardized connections for electronic devices, MCP provides a standardized way for AI applications to connect to external systems.
Although MCP claims to be a protocol, it’s actually a standard architecture for connecting traditional applications with large models.
In-Depth Comparison and Replacement Analysis
Technical Level
MCP was launched by Claude in December 2024. After nearly a year of development, the MCP ecosystem has become quite prosperous. However, from a technical perspective, MCP’s shortcomings are quite obvious.
1. Significant Difference in Token Usage
MCP’s Problem:
- GitHub’s official MCP alone consumes tens of thousands of context tokens
- Once you add a few more MCPs, there’s little room left for LLM to do actual useful work
Claude Skills Advantage:
- Can offload context to the file system
- Greatly optimizes interaction between application and model layers in consistency, performance, and cost
2. Fundamental Limitations in Prompting
The core of MCP is the MCP Server definition. After Andrej Karpathy proposed context engineering, comparing MCP’s core definition reveals:
MCP’s Limitation:
- Uses prompt engineering approach to connect applications and large models
- This approach was correct last year, but in 2025, it’s clearly not enough
New Perspective on Context Engineering: According to Peak Ji, chief scientist at Manus, conclusion from extensive engineering practice:
Currently, context engineering is the clearest, most practical boundary between applications and models.
Claude Skills are designed based on this philosophy.
3. Declining Appeal
With capabilities like Claude Code and Agent SDK emerging, truly worthwhile MCPs are actually not many.
Commonly Used MCPs:
- Context7
- Playwright
- Browser MCP
- MCPs assisting AI coding
- Official MCPs for maps, databases and other common middleware
Reality: MCP’s appeal to individual developers is weakening.
Application Perspective
Especially in 2025 with AI Agent explosion, more and more Agent products are emerging, foundation model companies are emphasizing Agentic capabilities, especially coding abilities.
Market Status:
- AI Agents or tools with MCP as a core selling point are actually not many
- Most still serve various AI Coding vertical domains
- Threshold for developing an AI Agent is getting lower
- Connecting models to traditional applications is less attractive than directly rebuilding traditional apps with AI
- Can also tell a better commercialization story
Short-Term Integration, Long-Term Replacement?
Will Claude Skills Replace MCP?
Not in the short term, will take integration route.
Claude, as the launcher of MCP, certainly doesn’t want to contradict itself. So, looking at the official Claude Skill architecture:
Official Design Philosophy:
- MCP handles connections to external applications
- Skills handle definable internal applications (or components) with independent runtime environments
- MCP, Skills, and Claude Code together form Claude’s Agent ecosystem
Rise of Skills Market
Foreseeable Development:
- Skills can be shared
- There will definitely be a gradually prosperous Skill Market, just like MCP Market
- Claude officially provided example Skills for PPT, Excel, Word, PDF
Market Question:
- There are also these types of MCPs in the market now
- Since Skills are more stable and reliable, why not use them?
Potential Standardization Issues
Application Layer Concerns:
- If Skill isn’t a standard, application layer has risk of being locked to Claude model
- Claude’s pricing is indeed quite expensive
Future Outlook:
- If Skill is a good idea, other foundation model companies will likely launch their own Skill-like capabilities in the coming months
- This might form new industry standards
Mid to Long-Term Trends
MCP’s Changing Value: In the mid to long term, MCP’s value will decrease.
Claude’s Follow-Up Actions: Claude also seems to have some actions to adapt to this change.
Rapid Evolution of Context Engineering
Reflecting on this, context engineering evolved from concept to implementation in one year, and quickly established an ecosystem—this progress is quite fast.
Technology Evolution Insights:
- Late 2024: MCP launched, based on prompt engineering approach
- Early 2025: Context engineering philosophy matured
- Late 2025: Claude Skills launched, based on context engineering
- Future: More innovations may emerge
Gemini 3’s Influence
Perhaps after Gemini 3 comes out, there will be some unexpected developments.
Possible Directions:
- More efficient context management mechanisms
- New capability extension paradigms
- Cross-model standardization attempts
Practical Recommendations
Current Selection Strategy
Use Claude Skills:
- Rapid prototyping
- Need deterministic output
- Focus on cost control
- Cross-platform deployment needs
Use MCP:
- Need to connect existing external systems
- Existing MCP ecosystem investment
- Standardized interface needs
Hybrid Use:
- Skills handle internal logic and document operations
- MCP connects external systems and data sources
- Fully leverage both advantages
Future Preparation
Follow Technology Trends:
- Continuously track Skills ecosystem development
- Observe other large model vendors’ moves
- Evaluate standardization possibilities
Flexible Architecture Design:
- Avoid over-dependence on single technology
- Maintain architectural extensibility
- Reserve space for technology switching
Summary
Will Claude Skills replace MCP?
Short Term (1-2 years):
- Won’t replace, but integrate
- MCP handles external connections
- Skills handle internal capabilities
- Jointly build Claude ecosystem
Mid Term (2-3 years):
- MCP’s value will decrease
- Skills ecosystem will be more prosperous
- New industry standards may emerge
- Other large model vendors will follow
Long Term (3+ years):
- New unified standards may form
- Context engineering will further evolve
- AI capability extension will have new paradigms
Core Viewpoint:
Don’t see this as a “replacement” relationship, but as a natural process of technology evolution. MCP pioneered standardized connections, Skills advanced further in efficiency and usability. Better solutions may emerge in the future—this is the charm of technological progress.
Reference Resources
Final Thought:
Context engineering evolved from concept to practice in one year, from MCP to Skills, demonstrating AI technology’s rapid iteration. As developers, we need to maintain an open mindset, embrace change, while building flexible architectures to adapt to future technology evolution.