The Future of AI Integration

Why MCP Matters

Recent setbacks in general-purpose AI development - GPT-5's postponements and Llama 4's challenges - suggest we're hitting computational limits with current transformer networks. While "The Bitter Lesson" teaches us that general methods leveraging computation ultimately win, we believe the immediate future lies in specialized AI systems working together through standardized protocols.

This belief stems from two key observations:

1. Knowledge isn't freely available in one place - it's distributed across platforms, APIs, and specialized services
2. Different domains need different expertise - connecting specialized AI models through MCP is more effective than waiting for a single model to master everything

What We're Measuring

1. Models

Current language models excel at creative ideation but struggle with precise tool manipulation. Take Figma design: models can discuss design principles eloquently but fumble when actually operating Figma's interface. This gap exists because:

• Models lack specialized training in tool operations
• Training prioritizes general knowledge over practical mastery
• Sequential tool operations remain challenging
• Tool-specific workflows need improvement

2. MCP Clients

The client ecosystem spans desktop apps (Cherry Studio, SeekChat), web applications (AIaW, Chainlit), IDE integrations (VS Code, Zed), and mobile solutions. We evaluate their protocol support, tool integration, security implementation, and extension capabilities.

3. MCP Services

Services divide into two main categories:

• Local 🏠: File operations, browser automation, system control
• Cloud ☁️: APIs, project management, knowledge bases

Built across multiple languages (Python 🐍, TypeScript 📇, Go 🏎️, Rust 🦀, C# #️⃣, Java ☕), we evaluate their reliability, integration capabilities, and developer experience.

4. Service Routines

Complex workflows require careful evaluation:

Key Areas:

• Automation (Zapier, Home Assistant)
• Enterprise (Atlassian, Linear, Notion)
• Development (Git, IDE, Documentation)

How We're Testing

Our resource-conscious approach combines community input with AI automation:

Community-Driven Selection:

• Users submit and vote on test cases
• Quarterly voting refreshes priorities
• Focus on high-impact scenarios

Smart Resource Use:

• Test with top-performing components
• AI-powered execution and evaluation
• Automated reporting and analysis

Looking Ahead

MCP Benchmark aims to advance AI integration through rigorous, community-driven evaluation. Our next steps:

• Developing the database of components
• Voting and rating systems
• Deploy automated testing
• Release initial benchmarks

Together, we're building the foundation for more capable, distributed AI systems.