Why MCP is dead & How I vibe now

2026-01-24 · Source: AI Jason · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

The "Skills Plus CRI Tool" method significantly reduces token consumption for coding agents, achieving over 70% reduction by replacing traditional MCP (Multi-Context Provider) tools with a skill-based approach. This method leverages agent skills, which are small prompt snippets and resource lists injected contextually, adding only 10-50 tokens per skill compared to the large context windows consumed by MCPs. The article highlights how this approach, exemplified by Manis and Agent Browser, allows agents to execute complex tasks like browser testing more efficiently via Command Line Interface (CRI) packages. An open-source tool called MCP Porter facilitates migrating existing MCPs to this skill-based CRI framework, enabling developers to automate the conversion process and extend agent capabilities without performance degradation.

Key takeaway

For AI Engineers optimizing agent performance and cost, adopting the Skills Plus CRI Tool method is crucial. You should convert existing MCP tools to skill-based CRI packages using tools like MCP Porter to achieve significant token consumption reductions (over 70%) and enhance agent scalability. This approach allows your agents to access hundreds of integrations efficiently, making complex tasks more viable within limited context windows.

Key insights

Skill-based CRI tools drastically cut token consumption for coding agents, enhancing scalability and efficiency.

Principles

• Contextual skill injection minimizes token use.
• CRI enables flexible, powerful agent actions.
• Modular tools improve agent scalability.

Method

Create `skill.md` files containing prompt snippets and resource lists. Use CRI packages for tool execution. Migrate existing MCPs to CRI using tools like MCP Porter.

In practice

• Migrate MCPs to skill-based CRI with MCP Porter.
• Automate new MCP skill creation.
• Implement browser testing via CRI packages.

Topics

• AI Agent Skills
• Token Efficiency
• Command-Line Interface
• Cloud Co-work
• MCP Migration

Best for: AI Engineer, Machine Learning Engineer, Prompt Engineer

Related on AIssential

• Skills, Tools and MCPs - What’s The Difference? — AI agent capabilities evolve from basic function calls to standardized protocols and explicit expertise layers.
• Claude Skills - the SOP for your agent that is bigger than MCP — Claude Skills enhance agent capabilities by combining prompts with optional assets, significantly reducing token usage compared to MCPs.
• Agent Skills — Part 2: Bridging Skills with Production Tool Ecosystems — SkillRegistry decouples tool discovery from execution, enabling scalable agent skill management with existing production tools.
• CLI vs MCP: How AI Agents Choose the Right Tool for the Job — CLI excels for known commands, while MCP provides necessary abstraction and governance for complex tasks.
• How to correctly use MCP servers with your AI Agents — Careful MCP server integration prevents context bloat and optimizes AI agent performance and cost.
• AI Agents, Tools, MCP, and Skills: The Core, The Embellishment, and The Gimmick — AI agent development requires distinguishing core components like Agents and Tools from less practical elements like MCP.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Jason.