golf

作者 golf-mcp已验证

Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents

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2026/8/23
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⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/golf-mcp/golf

快速入门

使用 golf 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

Golf Banner


⛳ Golf

Easiest framework for building MCP servers


License PRs Support

📚 Documentation

Overview

Golf is a framework designed to streamline the creation of MCP server applications. It allows developers to define server's capabilities—tools, prompts, and resources—as simple Python files within a conventional directory structure. Golf then automatically discovers, parses, and compiles these components into a runnable MCP server, minimizing boilerplate and accelerating development.

With Golf v0.2.0, you get enterprise-grade authentication (JWT, OAuth Server, API key, development tokens), built-in utilities for LLM interactions, and automatic telemetry integration. Focus on implementing your agent's logic while Golf handles authentication, monitoring, and server infrastructure.

Quick Start

Get your Golf project up and running in a few simple steps:

1. Install Golf

Ensure you have Python (3.10+ recommended) installed. Then, install Golf using pip:

pip install golf-mcp

2. Initialize Your Project

Use the Golf CLI to scaffold a new project:

golf init your-project-name

This command creates a new directory (your-project-name) with a basic project structure, including example tools, resources, and a golf.json configuration file.

3. Run the Development Server

Navigate into your new project directory and start the development server:

cd your-project-name
golf build dev
golf run

This will start the MCP server, typically on http://localhost:3000 (configurable in golf.json).

That's it! Your Golf server is running and ready for integration.

Basic Project Structure

A Golf project initialized with golf init will have a structure similar to this:

<your-project-name>/
│
├─ golf.json          # Main project configuration
│
├─ tools/             # Directory for tool implementations
│   └─ hello.py       # Example tool
│
├─ resources/         # Directory for resource implementations
│   └─ info.py        # Example resource
│
├─ prompts/           # Directory for prompt templates
│   └─ welcome.py     # Example prompt
│
├─ .env               # Environment variables (e.g., API keys, server port)
└─ auth.py            # Authentication configuration (JWT, OAuth Server, API key, dev tokens)
  • golf.json: Configures server name, port, transport, telemetry, and other build settings.
  • auth.py: Dedicated authentication configuration file (new in v0.2.0, breaking change from v0.1.x authentication API) for JWT, OAuth Server, API key, or development authentication.
  • tools/, resources/, prompts/: Contain your Python files, each defining a single component. These directories can also contain nested subdirectories to further organize your components (e.g., tools/payments/charge.py). The module docstring of each file serves as the component's description.
    • Component IDs are automatically derived from their file path. For example, tools/hello.py becomes hello, and a nested file like tools/payments/submit.py would become submit_payments (filename, followed by reversed parent directories under the main category, joined by underscores).

Example: Defining a Tool

Creating a new tool is as simple as adding a Python file to the tools/ directory. The example tools/hello.py in the boilerplate looks like this:

# tools/hello.py
"""Hello World tool {{project_name}}."""

from typing import Annotated
from pydantic import BaseModel, Field

class Output(BaseModel):
    """Response from the hello tool."""
    message: str

async def hello(
    name: Annotated[str, Field(description="The name of the person to greet")] = "World",
    greeting: Annotated[str, Field(description="The greeting phrase to use")] = "Hello"
) -> Output:
    """Say hello to the given name.
    
    This is a simple example tool that demonstrates the basic structure
    of a tool implementation in Golf.
    """
    print(f"{greeting} {name}...")
    return Output(message=f"{greeting}, {name}!")

# Designate the entry point function
export = hello

Golf will automatically discover this file. The module docstring """Hello World tool {{project_name}}.""" is used as the tool's description. It infers parameters from the hello function's signature and uses the Output Pydantic model for the output schema. The tool will be registered with the ID hello.

Authentication & Features

Golf includes enterprise-grade authentication, built-in utilities, and automatic telemetry:

# auth.py - Configure authentication
from golf.auth import configure_auth, JWTAuthConfig, StaticTokenConfig, OAuthServerConfig

# JWT authentication (production)
configure_auth(JWTAuthConfig(
    jwks_uri_env_var="JWKS_URI",
    issuer_env_var="JWT_ISSUER", 
    audience_env_var="JWT_AUDIENCE",
    required_scopes=["read", "write"]
))

# OAuth Server mode (Golf acts as OAuth 2.0 server)
# configure_auth(OAuthServerConfig(
#     base_url="https://your-golf-server.com",
#     valid_scopes=["read", "write", "admin"]
# ))

# Static tokens (development only)
# configure_auth(StaticTokenConfig(
#     tokens={"dev-token": {"client_id": "dev", "scopes": ["read"]}}
# ))

# Built-in utilities available in all tools
from golf.utils import elicit, sample, get_context
# Enable OpenTelemetry tracing
export OTEL_TRACES_EXPORTER="otlp_http"
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318/v1/traces"
golf run  # ✅ Telemetry enabled

📚 Complete Documentation →

Configuration

Basic configuration in golf.json:

{
  "name": "My Golf Server",
  "host": "localhost",
  "port": 3000,
  "transport": "sse",
  "opentelemetry_enabled": false,
  "detailed_tracing": false
}
  • transport: Choose "sse", "streamable-http", or "stdio"
  • opentelemetry_enabled: Enable OpenTelemetry tracing
  • detailed_tracing: Capture input/output (use carefully with sensitive data)

Privacy & Telemetry

Golf collects anonymous usage data on the CLI to help us understand how the framework is being used and improve it over time. The data collected includes:

  • Commands run (init, build, run)
  • Success/failure status (no error details)
  • Golf version, Python version (major.minor only), and OS type
  • Template name (for init command only)
  • Build environment (dev/prod for build commands only)

No personal information, project names, code content, or error messages are ever collected.

Opting Out

You can disable telemetry in several ways:

  1. Using the telemetry command (recommended):

    golf telemetry disable
    

    This saves your preference permanently. To re-enable:

    golf telemetry enable
    
  2. During any command: Add --no-telemetry to save your preference:

    golf init my-project --no-telemetry
    

Your telemetry preference is stored in ~/.golf/telemetry.json and persists across all Golf commands.

Made with ❤️ in Warsaw, Poland and SF

常见问题

What is golf?

golf is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by golf-mcp. Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents. It has 837 GitHub stars.

Is golf safe to use?

Yes. golf passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install golf?

Clone the repository with "git clone https://github.com/golf-mcp/golf" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is golf written in?

golf is primarily written in Python. It is open-source under golf-mcp on GitHub, so you can review or fork the full source.

Are there alternatives to golf?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh golf against similar tools.

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