MCP Kotlin Server

Overview

The MCP Server Public is a lightweight, Kotlin‑based Model Context Protocol (MCP) server that exposes two practical capabilities for AI assistants: weather information retrieval and email notification. By integrating these services into the MCP framework, developers can seamlessly extend AI assistants with real‑world data and communication features without writing custom code for each platform.

Solving a Common Integration Gap

AI assistants often need to pull up‑to‑date contextual data or trigger external workflows. However, many developers struggle to expose third‑party APIs in a way that is consumable by MCP clients. This server bridges that gap by providing ready‑made tools for obtaining weather details and sending emails, both of which are common requirements in chatbots, virtual assistants, and automated support systems.

What the Server Does

• Weather Retrieval – The server accepts a city name, uses geocoding to translate it into geographic coordinates, and then queries a weather service (e.g., OpenWeatherMap) to return current conditions. The response includes temperature, humidity, wind speed, and a concise description, allowing the AI to give users accurate, localized weather updates.
• Email Notification – Leveraging the Resend SDK, the server can send templated or custom emails. This is useful for confirming actions, sending alerts, or delivering personalized content directly from the AI assistant’s conversation flow.

Both tools are exposed as MCP resources, so any Claude or other MCP‑compliant client can invoke them by name and pass the required parameters in a structured JSON payload.

Key Features Explained

• Kotlin SDK Integration – Built with Kotlin, the server benefits from strong type safety and concise syntax. The Gradle build system compiles the project into a deployable JAR, making it easy to run on any JVM‑compatible host.
• Modular Design – Each capability is encapsulated in its own service class, enabling developers to add or replace functionalities without touching the core MCP plumbing.
• Resend Email SDK – The email tool uses a modern, well‑maintained SDK that handles authentication, templating, and delivery metrics, reducing the risk of bounced or malformed messages.
• Geocoding First – By obtaining latitude and longitude before requesting weather data, the server ensures that city names are resolved accurately even when multiple locations share a name.

Real‑World Use Cases

1. Travel Assistants – A user asks, “What’s the weather in Barcelona?” The AI calls the MCP server, receives current conditions, and presents them conversationally.
2. Appointment Scheduling Bots – After confirming a meeting, the bot triggers an email reminder via the MCP server, ensuring the user receives timely notifications.
3. IoT Control Interfaces – A smart home assistant can query the weather to adjust thermostat settings, while sending an email alert if unusual conditions are detected.
4. Customer Support – When a support agent resolves an issue, the assistant can automatically email a follow‑up survey through the MCP server.

Integration with AI Workflows

The MCP protocol defines a simple request/response contract. An AI client constructs a JSON payload specifying the tool name ( or ) and parameters, sends it to the server’s endpoint, and receives a structured response. The assistant can then weave this data into its next turn of dialogue. Because the server’s API is stateless and follows MCP conventions, it can be hosted behind any load balancer or serverless platform, allowing horizontal scaling as user demand grows.

Unique Advantages

• Zero Boilerplate for Common Tasks – Developers no longer need to write separate adapters for weather APIs or email services; the MCP server bundles them into a single, well‑documented interface.
• Type Safety and Reliability – Kotlin’s null safety guarantees that missing parameters or malformed inputs are caught early, reducing runtime errors in production deployments.
• Extensibility – Adding a new tool (e.g., calendar integration, translation service) follows the same pattern: implement a Kotlin class and expose it via MCP. This modularity encourages rapid feature expansion.

In summary, the MCP Server Public offers a pragmatic solution for embedding weather data and email notifications into AI assistant workflows. Its Kotlin foundation, clean modular design, and adherence to MCP standards make it an attractive choice for developers seeking reliable, scalable integrations without the overhead of custom API wrappers.