If you want a Model Context Protocol server that does not fall over at the first sign of traffic this guide walks you through a pragmatic Java and Spring Boot setup that forwards MCP requests to Cursor AI or any compatible model backend while handling streaming and basic API security without drama.

What you will build

A tidy MCP server that accepts Model Context Protocol requests via a REST API and forwards them to a model provider using Spring WebClient. It supports both standard and streaming responses and includes a minimal API key or bearer token check so you do not accidentally become the open internet model proxy.

Project setup

Start with Java 17 and a Spring Boot app. Add the essentials such as spring-boot-starter-web, Jackson for JSON, and validation support. Choose Maven or Gradle and keep your package structure sane. The server entry point is intentionally small and honest about its job which is to receive MCP payloads and pass them along.

• Java 17 runtime
• Spring Boot web starter
• Jackson and validation
• Reactor Netty for WebClient streaming

Define MCP DTOs

Create request and response classes that map to the Model Context Protocol schema. Use validation annotations on required fields and lean on Jackson for any serialization quirks. Keep DTOs immutable where convenient and avoid sending giant object graphs unless you like debugging in the dark.

Request and response shapes

Design DTOs for incoming MCP requests with fields you expect from clients and a response wrapper that can carry both synchronous results and streaming fragments. Validate payloads at the controller boundary so the rest of the app can assume a sane contract.

Controller wiring and request handling

Expose a POST endpoint that maps the body to your MCP request DTOs. Keep the controller thin. Validate input and then hand the heavy lifting to a service that mediates between your HTTP layer and the model provider. That service can handle retries, timeouts, and parse streaming chunks into a form your API can forward.

• Use @PostMapping for the MCP endpoint
• Validate with @Valid and binder annotations
• Delegate to a ModelService for provider calls

WebClient integration and streaming support

Use Spring WebClient to call the model provider. WebClient plays nicely with Reactor so you can stream responses as Flux elements. Support both standard JSON responses and streaming responses so clients can receive tokens or events as they arrive. Add timeouts and backpressure handling because model providers have moods and deadlines matter.

Streaming tips

• Use WebClient retrieve and exchangeToFlux for streaming responses
• Map server side Flux elements to Server Sent Events or chunked transfer so HTTP clients can consume partial output
• Apply backpressure and timeouts to avoid leaking resources

API security

Add a simple filter or interceptor to check an API key or bearer token on incoming requests. Reject invalid requests early with 401 or 403. Keep keys out of logs and rotate them like you mean it. For slightly more seriousness add rate limits and monitoring but this guide keeps things minimal so you can get to the fun part which is forwarding model requests.

Testing and running locally

Write unit tests for controllers and services and an integration test that mocks the model provider. You can mock WebClient responses or use a lightweight mock server. Run the app locally and exercise the endpoints with curl or any HTTP client. Check both normal payloads and streaming cases so you do not ship surprises to production.

Summary

This guide covered a practical MCP server setup using Java and Spring Boot that wires DTOs to controllers, forwards requests to a model provider via WebClient, supports streaming, and adds basic API security. It is not magic but it is honest work and it will keep your Cursor AI integration neat and testable.

If you want code examples or a sample project structure next up say the word and I will hand you a starter repo that will save you from copy pasta and existential debugging.