MCP Tool Template

The MCP Tool Template is a ready‑made starting point for developers who want to create Model Context Protocol (MCP) tools that can be seamlessly integrated into AI assistants such as Claude. MCP is a specification that standardizes how external services expose capabilities—resources, tools, prompts, and sampling—to AI clients. By following this template, teams can focus on business logic while ensuring that the resulting tool complies with MCP’s interface contracts and schema validation rules.

What Problem Does It Solve?

Building an MCP tool from scratch involves a lot of boilerplate: defining input and output schemas, wiring up the protocol interface, and writing tests to guarantee that the tool behaves correctly when called by an AI agent. The template removes this friction by providing a structured directory layout and example tooling code that already implements the necessary MCP plumbing. Developers no longer need to reinvent the wheel for each new tool; instead, they can concentrate on implementing domain‑specific functionality (e.g., calculations, data retrieval, or custom prompts) and rely on the template’s proven patterns for compatibility.

What The Server Does

When deployed, an MCP server built from this template exposes one or more tools that AI assistants can invoke. Each tool is defined by a TypeScript module that implements the MCP protocol interface and declares its input/output schemas with Zod. The server handles request routing, schema validation, and response formatting automatically. For developers, this means that the server acts as a reliable bridge between an AI assistant’s request payload and the underlying business logic, guaranteeing that data contracts are respected and errors are surfaced in a consistent manner.

Key Features & Capabilities

• Schema‑driven validation: Input and output schemas are defined using Zod, ensuring that every request is validated before business logic runs.
• Modular tool structure: The directory encourages a clear separation of concerns, making it simple to add or remove tools without affecting others.
• Extensive testing scaffold: The template includes a test harness that runs unit tests against each tool, verifying both schema compliance and functional correctness.
• Prompt integration: Tools can expose custom prompts, allowing AI assistants to receive context‑rich instructions tailored to the tool’s purpose.
• Resource and sampling support: While the core template focuses on tools, it is designed to be extended with additional MCP concepts such as resources or sampling strategies.

Use Cases & Real‑World Scenarios

• Automated calculations: Create a calculator tool that performs arithmetic or statistical operations, enabling an AI assistant to answer numeric queries on demand.
• Domain‑specific data access: Build tools that query internal databases or APIs, allowing the assistant to fetch up‑to‑date information (e.g., inventory levels, weather data).
• Custom prompt generators: Supply context‑aware prompts that guide the assistant’s language generation for specialized tasks like legal drafting or medical triage.
• Workflow orchestration: Combine multiple MCP tools into a single workflow, letting the assistant coordinate complex tasks across different services.

Integration with AI Workflows

Once a tool is deployed, an MCP‑compliant client (such as Claude) can discover the tool via the server’s endpoint, automatically understand its schema, and invoke it during a conversation. The protocol guarantees that the assistant’s prompt engineering can incorporate tool calls transparently, resulting in smoother interactions and higher reliability. Developers benefit from a standardized contract that eliminates the need for custom adapters or manual serialization logic.

Standout Advantages

The template’s biggest advantage is its developer‑first design. By bundling best practices—schema validation, test coverage, and a clean directory layout—it reduces the learning curve for teams new to MCP. Additionally, because all components are written in TypeScript and adhere strictly to the MCP spec, the resulting tools are type‑safe, maintainable, and ready for production deployment with minimal friction.