Mcp Ts Template

Overview

The mcp-ts-template is a production‑grade, TypeScript‑first scaffold for building Model Context Protocol (MCP) servers. It solves the recurring pain of wiring together tools, resources, prompts and authentication in a clean, type‑safe way while keeping the codebase maintainable across both local development and edge deployments such as Cloudflare Workers. By providing a declarative registry, unified error handling, and pluggable storage and auth mechanisms, the template lets developers focus on crafting business logic rather than boilerplate.

At its core, the server exposes a set of tools—self‑contained functions that can be invoked by an AI assistant. The template ships with five illustrative tools, including a simple echo tool, a cat‑fact fetcher, and an LLM‑driven code review sampler. Each tool can declare required parameters; the framework automatically prompts users for missing values, enabling interactive elicitation. Resources such as give static or computed data that can be referenced by tools, while prompts provide reusable text templates for LLM calls. The declarative approach means a new capability can be added by creating a single file, registering it with the framework, and the server will handle routing, validation, and execution.

Key capabilities are wrapped in a robust, type‑safe API. A unified system guarantees consistent error responses, and the optional OpenTelemetry integration offers structured tracing and metrics out of the box. Storage is abstracted, so developers can switch between in‑memory, filesystem, Supabase, or Cloudflare KV/R2 without touching business logic. Authentication is equally flexible: the server can run in , , or mode, allowing secure deployments for internal tooling or public APIs.

The template is designed to be edge‑ready. All code can run locally under Bun or in a Cloudflare Worker with no modification, enabling low‑latency deployments close to users. Dependency injection via keeps the architecture testable, and built‑in utilities for parsing PDFs, YAML, CSV, scheduling, and security reduce the need for external libraries. Services for LLM providers (e.g., OpenRouter) and text‑to‑speech APIs (ElevenLabs) are pre‑wired, so developers can integrate advanced AI features with minimal effort.

In practice, this MCP server is ideal for teams building internal assistants that need to call external APIs, perform LLM inference, or manipulate data on the fly. Use cases include automated code review bots, interactive storytelling assistants, real‑time data dashboards, or any scenario where an AI assistant must orchestrate multiple services while maintaining a consistent conversational context. By leveraging the template’s declarative tooling, robust error handling, and edge deployment support, developers can rapidly iterate on AI workflows without reinventing the MCP plumbing.