BambooHR MCP Server

BambooHR MCP – A Type‑Safe Bridge Between AI Assistants and Human Resources Data

The BambooHR MCP server addresses a common pain point for developers building AI‑powered workplace tools: accessing HR data in a reliable, typed, and secure way. Traditional integrations rely on raw REST calls that expose sensitive credentials and return unstructured JSON, forcing developers to write repetitive parsing logic. This MCP server abstracts those complexities behind a promise‑based API that validates every request against BambooHR’s own schema, ensuring that the data your AI assistant consumes is both accurate and up‑to‑date.

At its core, the server exposes a handful of high‑level helpers—such as , , and —that map directly to BambooHR’s most frequently used endpoints. Each helper accepts the API token and company domain, or reads them from environment variables, and returns fully typed TypeScript objects. This type safety is not merely a convenience; it prevents subtle bugs that can arise when an AI assistant misinterprets fields like or , which could lead to incorrect reporting or data leaks. By returning promises, the API also fits naturally into async workflows that AI assistants often use to chain multiple data sources together.

Developers can leverage this MCP in a variety of real‑world scenarios. For instance, an AI assistant could generate daily attendance summaries by calling , or it could automate time‑tracking entries for a project by first retrieving the relevant task IDs via and then posting hours with . Because the server handles authentication internally, the assistant’s prompts can remain focused on business logic rather than credential management. Additionally, the clear separation of concerns—API calls versus AI prompt generation—makes it straightforward to extend the server with custom endpoints or to swap out BambooHR for another HR system without changing the assistant’s core logic.

What sets this MCP apart is its commitment to developer ergonomics. The library ships with exhaustive TypeScript definitions for every model and response, eliminating the need to manually write interfaces. Its promise‑based design aligns with modern JavaScript patterns, and the modular structure encourages adding new methods in a single file while keeping exports tidy. For teams already using Node.js or TypeScript, integrating the BambooHR MCP is a matter of importing a few functions and wiring up environment variables—no heavy scaffolding required.

In summary, the BambooHR MCP server empowers AI assistants to interact with human resources data safely and efficiently. By providing a typed, promise‑based abstraction over BambooHR’s REST API, it removes boilerplate, reduces errors, and lets developers focus on delivering intelligent, context‑aware experiences in the workplace.