Canvas MCP 是一套工具，允许您的 AI 代理与 Canvas LMS 和 Gradescope 进行交互。
查找相关资源- 能够使用自然语言查找给定查询的相关资源！
查询即将到来的作业- 不仅获取即将到来的作业，还提供给定课程的细目分类。
从 Gradescope 获取课程和作业- 使用自然语言查询您的 Gradescope 课程和作业，获取提交状态等等！
获取课程
获取模块
获取模块项
获取文件 URL
获取日历事件
获取作业
还有更多...
Related MCP server: Canvas Assignment Assistant
事先记下以下事项：
Canvas API 密钥来自Canvas > Account > Settings > Approved Integrations > New Access Token
Gemini API 密钥来自https://aistudio.google.com/app/apikey
Gradescope 电子邮件和密码https://www.gradescope.com/
要通过Smithery自动为 Claude Desktop 安装 Canvas MCP：
npx -y @smithery/cli install @aryankeluskar/canvas-mcp --client claude或者，对于 Cursor IDE，将 canvas-mcp 与其他模型一起使用：
npx -y @smithery/cli install @aryankeluskar/canvas-mcp --client cursor或者，对于 Windsurf：
npx -y @smithery/cli install @aryankeluskar/canvas-mcp --client windsurf下载存储库并运行以下命令：
git clone https://github.com/aryankeluskar/canvas-mcp.git
cd canvas-mcp
# Install dependencies with uv (recommended)
pip install uv
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt
# Or install with pip
pip install -r requirements.txt在根目录中创建一个.env文件，其中包含以下环境变量：
CANVAS_API_KEY=your_canvas_api_key
GEMINI_API_KEY=your_gemini_api_key将以下内容添加到您的mcp.json或claude_desktop_config.json文件中：
{
"mcpServers": {
"canvas": {
"command": "uv",
"args": [
"--directory",
"/Users/aryank/Developer/canvas-mcp",
"run",
"canvas.py"
]
}
}
}由Aryan Keluskar建造 :)
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