canvas-mcp
Enables MCP-compatible clients to read courses, assignments, announcements, files, and grades from a Canvas LMS instance.
README
canvas-mcp
An MCP server that exposes Canvas LMS data (courses, assignments, announcements) to any MCP-compatible client (Claude Desktop, Claude Code, etc.).
This repository ships with no credentials. There is no Canvas token and no email address anywhere in it. You supply your own token in a local
.envfile that is git-ignored and never leaves your machine. See Where your token goes.
Prerequisites
- uv (Python package manager)
- A Canvas LMS account with an API token
Setup
1. Generate a Canvas API token
- Log in to your school's Canvas site (for example
https://canvas.fen.uchile.cl) - Go to Account → Settings → Approved Integrations
- Click + New Access Token, give it a name, and copy the token
2. Configure the environment
<a id="where-your-token-goes"></a>
Where your token goes: in a file named exactly .env, in the root of this
project folder (right next to main.py). That file is listed in .gitignore,
so git will never commit or push it.
Copy the template:
cp .env.example .env
Then open .env and fill in the blanks:
# Your Canvas instance URL -- change this to your school's Canvas address
CANVAS_URL=https://canvas.fen.uchile.cl
# Paste the token from step 1 here
CANVAS_API_TOKEN=
# Optional second account (use account="udd" in the tools). Leave blank if unused.
CANVAS_UDD_URL=
CANVAS_UDD_TOKEN=
Only CANVAS_URL and CANVAS_API_TOKEN are required. The CANVAS_UDD_* pair is
for an optional second Canvas account; leave both blank if you only have one.
Never commit
.env, never paste your token into a chat, and never put it in a file that gets shared. It grants full access to your Canvas account. If it leaks, delete the token in Canvas (Account -> Settings -> Approved Integrations) and generate a new one.
3. Install dependencies
uv sync
4. Run the server manually (optional test)
uv run python main.py
The server communicates over stdio, so it won't print anything until an MCP client connects.
Connecting to Claude Desktop
Add the following to your Claude Desktop config file:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"canvas": {
"command": "uv",
"args": ["run", "--project", "/absolute/path/to/canvas-mcp", "python", "main.py"]
}
}
}
Replace /absolute/path/to/canvas-mcp with the real path to this folder. Use
forward slashes / even on Windows.
No token goes in this file. It is read from your
.env(see Where your token goes) --python-dotenvloads it automatically.
Connecting to Claude Code
Add the MCP server in your Claude Code settings or run:
claude mcp add canvas -- uv run --project /absolute/path/to/canvas-mcp python main.py
Available Tools
| Tool | Description |
|---|---|
get_courses |
Lists all active enrolled courses |
get_assignments(days_ahead=30) |
Lists upcoming assignments sorted by due date |
get_announcements |
Lists recent announcements per course |
Project structure
canvas-mcp/
├── main.py # MCP server
├── .env # Your token lives here (git-ignored, never pushed)
├── .env.example # Template
├── pyproject.toml # Project metadata & dependencies
└── uv.lock # Locked dependency versions
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。