From COM to MCP: The Evolution of Interoperability and the Rise of Model Context Protocol

When I began my journey in software development back in 1998, working with Visual C++, one of the most revolutionary concepts introduced by Microsoft at that time was COM (Component Object Model). It was soon followed by COM+ and DCOM, laying the foundation for interoperability between software components on Windows platforms. The idea was powerful: decouple software into components that could be built independently and still work seamlessly together — invoking functions, sharing data, and scaling across systems.

But that was just the beginning.

A Brief History of Interoperability

The Java Era

Soon, Java entered the scene with its own set of interoperability tools:

• RMI (Remote Method Invocation) enabled Java objects in different JVMs to call each other's methods as if they were local.

• JNI (Java Native Interface) bridged Java code with native applications written in C/C++.

• CORBA (Common Object Request Broker Architecture) tried to unify communication between systems regardless of language, platform, or architecture.

These technologies made distributed computing real — though often complex and heavyweight.

The Rise of the Web and Service-Oriented Architecture

As the internet exploded in the early 2000s, the focus shifted toward Web Services. This gave birth to:

• SOAP (Simple Object Access Protocol) with XML-based messaging,

• WSDL and UDDI for service definitions and discovery,

• And eventually, RESTful APIs became the default approach — lightweight, stateless, and language-agnostic.

This was the era of Service-Oriented Architecture (SOA), and it drove another leap forward in how systems talked to each other.

The Rise of MCP and the Limitations of REST for AI

RESTful APIs are inherently stateless and resource-oriented, requiring explicit client-side logic for managing context across interactions. For AI agents, which thrive on dynamic discovery, continuous context, and autonomous tool utilization, this poses significant challenges. LLMs often struggle with:

• Dynamic Tool Discovery: Traditional APIs require pre-programmed knowledge of endpoints, parameters, and expected responses. AI agents need to discover capabilities at runtime.

• Context Management: Maintaining a coherent conversation or task across multiple API calls is complex with stateless REST.

• Standardized Interaction: Different REST APIs have varying authentication, error handling, and data formats, leading to fragmented integration efforts for AI.

• Tool Selection Constraints: Presenting an LLM with a vast array of granular REST endpoints can overwhelm its decision-making process.

MCP addresses these challenges by introducing three fundamental primitives:

• Tools: Discrete functions or actions an AI agent can invoke dynamically, self-describing their functionality, inputs, and outputs.

• Resources: Read-only data sources that agents can query for context, such as documentation, configuration, or real-time data.

• Prompt Templates: Pre-defined instructions that guide the AI's interaction with users for specific tasks.

Gen AI and a New Paradigm: Model Context Protocol (MCP)

Today, we’re entering yet another phase of evolution — fueled by Generative AI and multi-agent collaboration. As AI models like GPT, Claude, or open-source LLMs become tools in themselves, interoperability no longer means just services talking to each other — now it means:

• Models talking to models,

• Agents calling APIs dynamically,

• Human-like reasoning happening across modular toolchains.

This is where the Model Context Protocol (MCP) comes into play.

MCP is designed as a standardized mechanism for models and agents to discover, invoke, and use external tools/APIs using a declarative configuration. It's similar in spirit to how Swagger/OpenAPI made API documentation consumable by machines — but designed specifically for tool-augmented AI workflows.

Converting RESTful APIs to MCP Servers

The conversion of existing RESTful APIs to MCP Servers doesn't necessarily mean rewriting your entire backend. Instead, it often involves an "abstraction layer" or "Server Extensions" that expose your existing REST functionality as MCP primitives. This can be achieved through:

• API Management Gateways: Platforms like Azure API Management are increasingly offering built-in capabilities to expose REST APIs as MCP servers. This simplifies the process by centralizing control over authentication, authorization, and monitoring, effectively acting as the "server extension. Source - https://learn.microsoft.com/en-us/azure/api-management/export-rest-mcp-server

• Custom Adapters/Wrappers: Developers can build custom MCP server implementations that internally call existing REST API endpoints. These adapters translate MCP requests into REST calls and format REST responses into MCP-compatible outputs. Tools like AutoMCP leverage OpenAPI specifications to automate the generation of such server stubs. We can use FastMCP framework in Python and wrap the existing RESTful service to MCP server, it can further automated using YML. Source https://gofastmcp.com/getting-started/welcome

• Framework-Specific Integrations: Some web frameworks (e.g., FastAPI in Python) can be extended to directly expose endpoints as MCP tools and resources, leveraging the existing framework's capabilities. Example - https://github.com/tadata-org/fastapi_mcp

The conversion of existing RESTful APIs into MCP Servers via Server Extensions is not a replacement of REST but an evolution. It's about creating an intelligent abstraction layer that empowers AI agents to seamlessly interact with the digital world. While it introduces new considerations for developers, the benefits in terms of AI integration, flexibility, and developer productivity make it a crucial step in building the next generation of AI-powered applications.

From COM/DCOM to CORBA, SOAP, REST, and now MCP, every generation of developers has redefined what it means to build interoperable systems. In the era of Gen AI, we are no longer building services just for apps — we are building tools for models, agents, and AI-native systems.