Changyi's Blog

The Future of MCP: AI-Native Development

The Future of MCP: AI-Native Development

Future Vision

We are moving toward a world where AI is not just a helper—but a co-developer, co-operator, and co-pilot of every web application.

The Future I See

1. Web Apps Expose Capabilities as Standardized MCP Tools

Web applications will expose their capabilities as standardized MCP tools.

Instead of custom APIs for each integration, applications will provide:

  • Standard tool definitions that any MCP-compatible AI can use
  • Self-documenting capabilities that AI can discover automatically
  • Consistent interfaces across all applications

Example: A blog platform exposes tools like createPost, updatePost, publishPost as MCP tools. Any AI agent can use these tools without custom integration.

2. Universal Interface, No More Glue Code

Developers won't write glue code for different LLMs; MCP will be the universal interface.

  • One tool definition works with OpenAI, Anthropic, Google, and local models
  • No need to maintain multiple integration paths
  • Reduced complexity and maintenance burden

Impact: Development teams can focus on building features, not integrations.

3. AI Agents Build and Maintain Applications

AI agents will build and maintain large parts of our applications.

  • Automated development: AI writes code based on requirements
  • Maintenance: AI monitors, fixes bugs, and optimizes performance
  • Testing: AI generates and runs comprehensive test suites
  • Documentation: AI keeps documentation up-to-date

Example: An AI agent monitors your application, identifies performance issues, writes fixes, tests them, and deploys—all automatically.

4. Cloud Resources Controlled Through MCP-Based Agents

Cloud resources (databases, analytics, storage) will be natively controlled through MCP-based agents.

  • AI agents can manage infrastructure
  • Automated scaling and optimization
  • Intelligent resource allocation
  • Self-healing systems

Example: An AI agent monitors database performance, automatically scales resources, and optimizes queries based on usage patterns.

5. Every Project Has an "AI Operator"

Every serious project will come with an "AI operator" capable of running:

  • Maintenance: Regular updates and optimizations
  • Tests: Automated testing and validation
  • Migrations: Database and schema migrations
  • Data Workflows: ETL, analytics, reporting

This AI operator will be a first-class citizen in your development workflow, not just a helper.

6. LLMs Collaborate Across Tools and Services

LLMs will collaborate across tools, environments, and services through one shared protocol.

  • AI agents can work together on complex tasks
  • Share context and state across different services
  • Coordinate actions across multiple systems
  • Build distributed AI workflows

Example: Multiple AI agents collaborate to deploy a new feature—one handles code changes, another manages database migrations, a third updates infrastructure, and they coordinate through MCP.

MCP as Infrastructure

MCP is the infrastructure layer that enables this shift.

It's not just a protocol—it's the foundation of the next era of software development.

Why MCP Matters

  • Standardization: One protocol for all AI interactions
  • Interoperability: Works across providers and platforms
  • Safety: Built-in permissions and boundaries
  • Scalability: Designed for production use
  • Future-proof: Extensible and adaptable

The Paradigm Shift

Before MCP

  • AI as a feature (chat, text generation)
  • Custom integrations for each use case
  • Limited AI capabilities
  • Manual glue code

With MCP

  • AI as an operator (co-developer, co-pilot)
  • Standardized interfaces
  • Full AI capabilities
  • Automatic tool discovery and usage

Getting Ready

To prepare for this future:

  1. Learn MCP: Understand the protocol and its capabilities
  2. Expose Tools: Make your backend capabilities available as MCP tools
  3. Use MCP Tools: Integrate MCP-compatible tools into your workflow
  4. Build AI-First: Design applications with AI operators in mind

Conclusion

MCP represents a fundamental shift in how we think about AI and software development.

It's not about replacing developers—it's about augmenting them with AI capabilities that are:

  • Standardized: One interface for all AI interactions
  • Safe: Built-in permissions and boundaries
  • Powerful: Full access to tools and resources
  • Interoperable: Works across providers and platforms

The future is AI-native, and MCP is the foundation that makes it possible.

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