This blog discusses how organizations can modernize existing REST APIs with minimal effort by exposing them as tools via the Model Context Protocol (MCP). MCP is a universal standard that allows AI models to interact with external tools, data sources, and applications, effectively serving as a bridge between AI systems and the broader digital ecosystem. To modernize existing REST APIs, follow a 4-step process: Understand your application’s API surface: Use a well-documented REST API with an OpenAPI specification; Wrap your API with FastMCP: FastMCP is a lightweight server that automatically converts API endpoints into tools that AI agents can understand and call; Deploy both your existing application and the MCP server together using Amazon Elastic Container Service (Amazon ECS) and AWS Fargate with a sidecar pattern; Build the AI agent with Strands Agents SDK: Strands provides a clean interface for connecting to MCP servers and integrating with Amazon Bedrock. To adapt this pattern for your existing applications, identify APIs with OpenAPI specs, evaluate which APIs you want AI agents to have access to, configure the MCP server, handle authentication and authorization, deploy using the sidecar pattern, and test and iterate. Benefits of this approach include minimal changes to existing applications, incremental modernization without a complete rewrite, scalable architecture, security by design, and cost-effectiveness. To get started with modernizing your own applications, review the Strands Agents SDK documentation, explore Amazon Bedrock for available AI models, identify applications with well-documented REST APIs, start with a proof of concept using a non-critical application, consider authentication and security requirements for production deployment, and limit API endpoints the agent has access to through FastMCP configuration options.