Reimagining the Model Context Protocol

This is follow up post after my first blog on model context protocol

The Model Context Protocol (MCP) has been instrumental in connecting AI models to real world via (tools,resources etc) , but what if we fundamentally reimagined it through the lens of REST architecture?

 Today, I'm proposing an alternative approach that leverages the proven patterns of RESTful design to create a more intuitive, scalable, and web-native solution. 

By treating AI capabilities as resources that can be uniformly addressed, manipulated, and discovered through standard HTTP methods, we can eliminate the complexity of the current JSON-RPC approach while gaining the benefits of caching, statelessness, and the vast ecosystem of tools built for REST APIs. This isn't just a technical refactoring—it's a philosophical shift that could make AI context management as approachable as browsing the web.

Little Recap on MCP

You can always read  model context protocol to learn more about MCP, but this is what it is at high level.

Trade-off when Comparing MCP (JSON-RPC Based) with a REST-Based Approach

1. Lack of resource-oriented modeling in current MCP
2. Non-standard interface semantics compared to HTTP methods
3. Limited built-in caching capabilities
4. Mixed stateful and stateless interaction patterns
5. Explicit versioning requirements rather than content negotiation
6. Need for specialized tooling instead of leveraging existing REST ecosystem
7. Custom error handling instead of standard HTTP status codes
8. Potential challenges with bidirectional communication in REST
9. Higher implementation complexity for developers
10. Limited self-discovery capabilities without hypermedia controls

What does REST based MCP looks like 

What are Key Benefits of REST based approach

This REST-based architecture offers:

• Simplicity: Familiar REST patterns reduce learning curve
• Discoverability: Self-documenting API with hypermedia controls
• Scalability: Stateless design enables horizontal scaling
• Caching: Efficient HTTP cache utilization
• Standards Compliance: Leverages established web standards
• Ecosystem Integration: Works with existing API infrastructure

Version of API spec is available at apispec.md

Conclusion

As we wrap up this exploration of architectural alternatives for the Model Context Protocol, it's clear that a RESTful approach offers compelling advantages over the current JSON-RPC implementation. 

By embracing HTTP's native semantics, resource-oriented design, and the vast ecosystem of tools built for REST APIs, we can create a more intuitive, discoverable, and web-friendly protocol. 

While some bidirectional communication patterns might require additional consideration, the benefits of standardisation, caching, and developer familiarity make REST a natural evolution for MCP. 

As AI systems continue to integrate more deeply with the broader software ecosystem, aligning our protocols with established architecture principles isn't just a technical choice—it's a strategic one that will lower barriers to entry and accelerate innovation in AI models integration.