Template MCP Server

Overview

The Template MCP Server provides a ready‑to‑use foundation for developers who want to expose custom tools, resources, and prompts to AI assistants through the Model Context Protocol (MCP). By leveraging the FastMCP framework, the template removes boilerplate and lets teams focus on building domain‑specific functionality rather than wiring up transports, serialization, or TypeScript configuration.

This server solves the common pain point of integrating bespoke services into conversational AI workflows. Developers can quickly spin up a local or remote MCP endpoint, define their own toolset (e.g., database queries, API wrappers, or calculation engines), and expose it to assistants like Claude. The result is a modular, reusable interface that keeps business logic isolated from the AI model while still enabling dynamic, real‑time interactions.

Key capabilities of the template include:

• Dual transport support: Run the MCP server over for local, command‑line usage or over Server‑Sent Events (SSE) via HTTP for networked, team‑shared deployments. This flexibility lets developers iterate locally and then expose the same codebase to collaborators or production environments.
• FastMCP integration: Built on FastMCP, the server inherits a lightweight, high‑performance runtime that abstracts away low‑level protocol details. Developers can declare tools, resources, and prompts in a structured way without managing sockets or message framing.
• TypeScript first: Full type safety across the entire codebase ensures that tool signatures, prompt templates, and resource definitions are validated at compile time, reducing runtime errors in production.
• Extensibility: The project scaffold includes clear directories for tools, resources, and prompts. Adding a new tool is as simple as exporting a function with the correct metadata; FastMCP automatically registers it for clients.

Typical use cases span from internal tooling to public APIs:

• Data‑driven assistants: Expose database queries or analytics dashboards as tools that an assistant can invoke on demand, enabling data‑centric conversations without exposing raw endpoints.
• Domain experts: Package specialized knowledge (e.g., legal compliance checks or medical guidelines) into prompts and resources, allowing assistants to reference authoritative content without hard‑coding it into the model.
• Collaborative workflows: Deploy the HTTP transport on a shared server so multiple developers or teams can add, update, and consume tools in real time, fostering a plug‑and‑play ecosystem around AI assistants.

Integration into existing AI workflows is straightforward. Once the server is running, clients such as Cursor or other MCP‑aware platforms discover it via a simple configuration file () or UI wizard. The assistant then sends context‑rich requests, receives structured responses, and can chain tool calls to achieve complex tasks—all while the server maintains stateful resources or performs side effects behind the scenes. This pattern keeps AI models focused on language understanding while delegating domain logic to specialized services, resulting in safer, more reliable, and maintainable conversational applications.