LiteMCP – A Lightweight, TypeScript‑First MCP Server

LiteMCP is a streamlined framework that lets developers expose Model Context Protocol (MCP) services with minimal ceremony. It addresses the common pain point of building MCP servers from scratch—managing tool, resource, and prompt definitions, handling errors, and providing a debugging console—all while staying fully typed with TypeScript. By encapsulating these concerns into a single, opinionated API, LiteMCP frees developers to focus on the logic of their AI‑enabled applications rather than boilerplate server plumbing.

At its core, LiteMCP offers a declarative way to register tools, resources, and prompts. A tool is a callable function that an LLM can invoke; it receives typed parameters defined with Zod, executes asynchronously, and returns a value that the model can consume. Resources expose arbitrary data—text files, logs, images, or binary blobs—to clients via a unique URI. Prompts are reusable templates that encapsulate common conversational flows, allowing the server to supply context‑aware prompts directly to an LLM or surface them in a UI. Together, these constructs give developers fine‑grained control over what their AI assistants can do and see.

LiteMCP shines in development workflows thanks to its built‑in CLI () and Server‑Sent Events (SSE) transport. The CLI provides a quick “dev” mode that runs the server, streams logs to the terminal, and offers interactive debugging. SSE support means clients can receive real‑time updates—such as streaming tool execution results or log messages—without the overhead of WebSocket or HTTP polling. The integrated logger lets servers emit structured logs (, , , ) that are forwarded to the client, enabling transparent monitoring of tool activity and resource access.

Real‑world scenarios that benefit from LiteMCP include:

• Enterprise tooling where an AI assistant needs to query internal log files or configuration resources.
• DevOps automation that exposes Git operations, CI/CD status checks, or deployment commands as MCP tools.
• Data‑driven chatbots that pull in dynamic reports or dashboards via resources and present them through prompts.
• Rapid prototyping of new LLM workflows, where developers can iterate on tool logic and prompt templates locally before deploying to production.

Because LiteMCP is written in TypeScript, developers enjoy compile‑time safety and IDE autocompletion for tool schemas and prompt arguments. The framework’s modular design means it can be dropped into existing Node.js services or paired with any MCP‑compatible client. In short, LiteMCP removes the friction of building an MCP server, allowing teams to quickly spin up powerful, typed AI integrations that scale from local debugging to cloud deployments.