Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools.
It provides a standardized method for connecting LLMs with necessary context in AI-based IDEs, chat interfaces, custom AI workflows, and more.
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Check out my project in the IDE and give me all the supported APIs of the project
- Create a urlShortener service
- Implement POST /api/shorten
- Generate a random 6-character string key consisting of only letters and numbers, starting with a letter
- Store in an in-memory hashmap and return the shortened URL with 200 OK
- Implement GET /api/shorten/{shortKey}
- When the endpoint receives a short key, return the corresponding original URL
- Structure with Controller, Service, and Repository layers
- Add convenience features:
- Create an http-client file for manual testing
- Add info logs to main flows
- Use io.github.oshai:kotlin-logging-jvm dependency for logging
- Maintain current logic for local env
- Use Exposed for local_postgres env (add dependency)
- The database for local_postgres env is already running via docker-compose
- Connection information is available in docker/docker-compose.yml
- Add environments in src/main/resources/application.yml
- local env: Continue using in-memory hashmap
- local_postgres env: Connect to PostgreSQL DB using Exposed
- Utilize PostgreSQL connection info from docker/docker-compose.yml
- Features:
- URL input form: Input field for long URLs and a 'Shorten' button
- URL list display: Show short key, original URL, and creation time
- Delete functionality: Add delete button to each URL entry
- Additional API endpoints:
- GET /api/shorten/urls: Retrieve all shortened URL listings
- DELETE /api/shorten/{shortKey}: Delete a specific shortened URL
- Workflow:
- Automatically refresh URL list after POST and DELETE operations
- Load initial URL list on page load