Kotlin Mcp Server

Overview

The Kotlin MCP Server is a lightweight, production‑ready implementation of the Model Context Protocol (MCP) tailored for Kotlin developers. It addresses a common pain point in AI‑powered application development: the need to expose domain logic, data sources, and custom prompts as first‑class services that an AI assistant can discover, query, and invoke without writing bespoke integration code. By running as a standard HTTP service that follows the MCP specification, this server allows any Claude‑compatible client to seamlessly discover its capabilities through a simple endpoint, then use those capabilities as if they were built‑in tools.

At its core, the server hosts resources, tools, and prompts that are defined in Kotlin. Resources represent reusable data models or stateful services (e.g., a user profile store), while tools are callable functions that perform actions such as database queries, external API calls, or business‑logic calculations. Prompts are pre‑formatted text snippets that can be injected into the AI’s context to steer its responses. The Kotlin SDK further simplifies development by providing type‑safe wrappers and annotations that map Kotlin functions to MCP tool definitions automatically, reducing boilerplate and minimizing the risk of mismatches between the server contract and client expectations.

Key capabilities include:

• Automatic tool registration via annotations, enabling rapid iteration on business logic without manual JSON schema updates.
• Rich type support for Kotlin data classes, ensuring that input validation and response serialization are handled transparently.
• Prompt templating with variable interpolation, allowing dynamic context injection based on runtime data or user intent.
• Secure execution with configurable authentication and request throttling, making it suitable for production environments where data privacy is paramount.

Typical use cases span from chat‑bot backends that need to retrieve and update user information, to internal tooling where an AI assistant can orchestrate CI/CD pipelines or generate documentation from codebases. For example, a customer support AI could call a tool that queries an order database and returns structured status updates, all while the underlying Kotlin service handles concurrency and persistence transparently.

Integration into existing AI workflows is straightforward: developers expose the MCP server alongside their application, then configure their Claude or other MCP‑compatible assistant to point at the server’s URL. The assistant automatically discovers available tools and prompts, presenting them as selectable actions in the UI or as callable functions within a conversational context. This plug‑and‑play model eliminates the need for custom middleware, allowing teams to focus on domain logic rather than protocol plumbing.