An MCP development environment needs an IDE or code editor, the package manager for the selected language, the appropriate SDK, and any API keys required by the model or external services.
The examples use editors such as Visual Studio Code, IntelliJ, and PyCharm. The SDKs are open source and provide the building blocks for clients, servers, tools, resources, and prompts.
The .NET examples include basic and advanced servers, ASP.NET integrations, and tool patterns. Java examples include Spring-ready builds, reactive programming, and type-safe error handling. JavaScript supports Node and browser development and includes WebSocket streaming. Python is asynchronous and can work with FastAPI or Flask and with machine-learning tools.
A practical learning sequence is:
- Create an MCP server.
- Inspect the server with MCP Inspector.
- Write a client that connects to the server.
- Add a language model so the client can negotiate with the server instead of sending only fixed
commands.
- Run the system in Visual Studio Code, including with GitHub Copilot agent mode where appropriate.
- Add server-sent events and then HTTP streaming for real-time responses.
- Use the AI Toolkit for Visual Studio Code to test and iterate.
- Test the complete implementation.
- Deploy the server locally or in the cloud.
A calculator agent is used as a practice project across languages. It is more than a minimal connection test because it gives the learner experience with tools, prompts, and resources while keeping the underlying task simple.
MCP Inspector is central to the development process. It connects to a running server, lists the available capabilities, and lets the developer invoke them directly. This makes it possible to inspect live server behavior before adding a complete model-driven host.
The Inspector can also connect to a deployed endpoint. The developer can list the server's tools and run them in real time, making it useful for both local development and remote verification.