Setting up the Project

In this section, we'll set up the development environment for our Pull Request Agent.

本节我们为 Pull Request Agent 搭建开发环境。

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We'll use modern Python tooling with uv for dependency management and create the necessary configuration files. If you're not familiar with uv, you can learn more about it here.

我们会使用现代 Python 工具链，用 uv 管理依赖并创建必要的配置文件。如果你还不熟悉 uv，可以在它的文档中进一步了解。

Project Structure

Let's start by creating the project directory and understanding the file structure:

我们先创建项目目录，并理解文件结构：

git clone https://huggingface.co/spaces/mcp-course/tag-this-repo

Our final project structure will look like this:

项目最终的结构大致如下：

hf-pr-agent/
├── mcp_server.py              # Core MCP server with tagging tools
├── app.py                     # FastAPI webhook listener and agent
├── requirements.txt           # Python dependencies
├── pyproject.toml             # Project configuration
├── env.example                # Environment variables template
├── cleanup.py                 # Development utility

Dependencies and Configuration

Let's walk through the dependencies and configuration for our project.

我们过一遍项目的依赖与配置。

1. Python Project Configuration

We will use uv to create the pyproject.toml file to define our project:

我们用 uv 生成 pyproject.toml 文件来定义项目：

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If you don't have uv installed, you can follow the instructions here.

如果还没有安装 uv，可以按其文档中的说明进行安装。

[project]
name = "mcp-course-unit3-example"
version = "0.1.0"
description = "FastAPI and Gradio app for Hugging Face Hub discussion webhooks"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
    "fastapi>=0.104.0",
    "uvicorn[standard]>=0.24.0",
    "gradio>=4.0.0",
    "huggingface-hub[mcp]>=0.32.0",
    "pydantic>=2.0.0",
    "python-multipart>=0.0.6",
    "requests>=2.31.0",
    "python-dotenv>=1.0.0",
    "fastmcp>=2.0.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src"]

For compatibility with various deployment platforms, the same is repeated in requirements.txt

为了兼容各种部署平台，同样的内容也在 requirements.txt 中重复一份。

To create a virtual environment, run:

创建虚拟环境，运行：

uv venv
source .venv/bin/activate # or .venv/Scripts/activate on Windows

To install the dependencies, run:

安装依赖，运行：

uv sync

2. Environment Configuration

Create env.example to document required environment variables:

创建 env.example，用来记录需要的环境变量：

# Hugging Face API Token (required)
# Get from: https://huggingface.co/settings/tokens
HF_TOKEN=hf_your_token_here

# Webhook Secret (required for production)
# Use a strong, random string
WEBHOOK_SECRET=your-webhook-secret-here

# Model for the agent (optional)
HF_MODEL=owner/model

# Provider for MCP agent (optional)
HF_PROVIDER=huggingface

You will need to get your Hugging Face API token from here.

你需要在这里获取 Hugging Face API 令牌（https://huggingface.co/settings/tokens）。

You will also need to generate a webhook secret. You can do this by running the following command:

你还需要生成一个 webhook 密钥，可以用下面的命令完成：

python -c "import secrets; print(secrets.token_hex(32))"

You will then need to add the webhook secret to your .env file based on the env.example file.

然后照着 env.example，把 webhook 密钥写进你的 .env 文件。

Next Steps

With our project structure and environment set up, we're ready to:

项目结构和环境就绪后，我们就可以开始：

1. Create the MCP Server - Implement the core tagging functionality
2. Build the Webhook Listener - Handle incoming discussion events
3. Integrate the Agent - Connect MCP tools with webhook processing
4. Test and Deploy - Validate functionality and deploy to Spaces

1. 创建 MCP 服务端——实现核心的打标签功能；2. 构建 webhook 监听器——处理传入的讨论事件；3. 整合智能体——把 MCP 工具与 webhook 处理连起来；4. 测试与部署——验证功能并部署到 Spaces。

In the next section, we'll dive into creating our MCP server that will handle all the Hugging Face Hub interactions.

下一节我们会深入创建处理所有 Hugging Face Hub 交互的 MCP 服务端。

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Keep your .env file secure and never commit it to version control. The .env file should be added to your .gitignore file to prevent accidental exposure of secrets.

妥善保管 .env 文件，切勿提交到版本控制。应把 .env 加入 .gitignore，避免密钥被意外泄露。