Golf

Golf is a lightweight yet powerful framework that turns a simple directory of Python files into a fully‑featured MCP server. By convention, developers place tools, resources, and prompt templates in dedicated folders; Golf automatically scans these directories, parses the module docstrings for descriptions, and registers each component with a deterministic ID derived from its path. This eliminates boilerplate configuration and lets teams focus on writing the business logic that powers their AI assistants.

The framework addresses a common pain point in AI‑tool integration: the need to manually wire up authentication, telemetry, and transport layers for every new server. With Golf v0.2.0, authentication is first class—supporting JWT, OAuth Server, API keys, and development tokens—all defined in a single file. Built‑in telemetry hooks stream usage data to observability backends, while the transport stack (HTTP/HTTPS) is configured via . Once these concerns are handled, developers can concentrate on the core logic of their agents.

Golf’s component model is intentionally minimal. Each tool or resource exposes a single callable that receives structured arguments from the client and returns a JSON‑serialisable result. Prompt templates are simple Python files whose docstrings describe the prompt text, allowing developers to version and iterate on prompts without touching server code. This design makes it trivial to add new capabilities or swap out implementations, fostering rapid experimentation and continuous delivery.

Real‑world use cases span from internal knowledge bases to external API orchestration. A company might expose a “get‑sales‑report” tool that queries an analytics database, or a “translate‑text” resource that wraps a third‑party translation API. In both scenarios, the MCP server serves as a secure gateway that an AI assistant can invoke with confidence, knowing that authentication and logging are handled automatically.

Because Golf is opinionated around directory structure and naming conventions, it integrates seamlessly into existing CI/CD pipelines. A simple command compiles the project, runs static checks, and produces a Docker image ready for deployment. The resulting server can be dropped into any AI workflow that supports MCP, enabling developers to ship new tools and prompts faster than ever while keeping the operational overhead low.