Local-first Canvas LMS MCP server. Stdio transport, ~125 lines of Python.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
claude mcp add canvas-mcp -- python -m canvas-local-mcp{
"mcpServers": {
"canvas-mcp": {
"command": "python",
"args": ["-m", "build"]
}
}
}Resumen de MCP Servers
# Canvas LMS MCP Server (`canvas-mcp`)
<!-- mcp-name: io.github.admin978/canvas-mcp -->
[](https://github.com/admin978/canvas-mcp/actions/workflows/ci.yml)
[](https://pypi.org/project/canvas-local-mcp/)
[](https://pypi.org/project/canvas-local-mcp/)
[](https://github.com/admin978/canvas-mcp/blob/main/LICENSE)
Ask Claude about your Canvas courses, assignments, deadlines, modules, and grades from one place.
`canvas-mcp` is a local-first MCP server for Canvas LMS users (students, instructors, and MCP builders). It turns Canvas REST API actions into MCP tools that work from Claude Code, Claude Desktop, and other MCP-compatible clients.
> **Status:** alpha. Single-user, no warranty, API surface may still shift. File issues if it breaks.
## Who this is for
- **Students** who want one view across multiple courses
- **Educators** who want faster access to assignments, modules, and announcements
- **MCP users** who want Canvas data in local Claude workflows
## What you can ask Claude
- “What assignments are due this week across all my active courses?”
- “Show upcoming events and planner items for next week.”
- “List my current grades by course.”
- “Get the modules (with items) for course `12345`.”
- “Show announcements for course `12345`.”
- “Fetch the page `syllabus` from course `12345`.”
## Quick start
Prerequisite: **Python 3.10+**.
### 1) Create a Canvas personal access token
In Canvas: **Account → Settings → Approved Integrations → + New Access Token**.
Copy the token shown (Canvas does not show it again later).
### 2) Install
From PyPI (recommended):
```bash
pip install canvas-local-mcp
```
Or from source:
```bash
git clone https://github.com/admin978/canvas-mcp.git && cd canvas-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
```
### 3) Configure `~/.canvas.env`
```bash
curl -fsSL https://raw.githubusercontent.com/admin978/canvas-mcp/main/.canvas.env.example -o ~/.canvas.env
chmod 600 ~/.canvas.env
# edit ~/.canvas.env: set CANVAS_BASE_URL (institution root, no /api/v1)
# and paste the token into CANVAS_TOKEN
```
For token safety guidance (least privilege, file permissions, rotation/revocation, and vulnerability reporting), see [SECURITY.md](SECURITY.md).
### 4) Register in your MCP client
Claude Code:
```bash
claude mcp add canvas-local -- canvas-local-mcp
```
Claude Desktop:
- macOS example path: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows example path: `%APPDATA%\Claude\claude_desktop_config.json`
- Linux example path: `~/.config/Claude/claude_desktop_config.json`
```json
{
"mcpServers": {
"canvas-local": {
"command": "canvas-local-mcp"
}
}
}
```
## Tools exposed
- `list_courses`
- `list_assignments`
- `list_modules`
- `list_announcements`
- `get_page`
- `get_file_info`
- `get_grades`
- `planner_items`
- `upcoming_events`
- `todo`
## Bulk dump
`canvas-local-mcp-dump` downloads course files and related content for offline indexing.
```bash
canvas-local-mcp-dump # all active courses
canvas-local-mcp-dump 12345 67890 # specific course IDs
```
Output goes to `./canvas-dump/` by default. Override with `CANVAS_DUMP_DIR=/path/to/dir`.
## Local-first, privacy and token flow
- The server runs locally and uses **stdio** transport with your MCP client.
- Configuration is read from `~/.canvas.env` (`CANVAS_BASE_URL`, `CANVAS_TOKEN`).
- Requests go from your local server to the official Canvas API endpoints.
- No external token broker is used in the request path.
- Security guidance and disclosure policy: [SECURITY.md](SECURITY.md).
## Demo
No demo GIF is currently committed yet. You can still verify the workflow quickly:
### Try these prompts
After registering the MCP server, ask Claude:
- “List my active Canvas courses.”
- “What assignments are due this week across all my active courses?”
- “Show upcoming events and planner items for next week.”
- “List my current grades by course.”
> Placeholder: add a short terminal/GIF walkthrough here in a future PR.
## Development
Requires **Python 3.10+**.
```bash
pip install -e ".[dev]"
ruff check canvas_local_mcp tests # lint
pytest # tests run against a mocked Canvas API — no token needed
```
CI runs lint + tests on Python 3.10–3.13 for every push and pull request.
## Publishing a new release
Follow this order to publish a new version (e.g. `0.1.4`) consistently across PyPI and the MCP Registry.
### 1. Bump the version everywhere
Update all three files to the new version string in a single PR:
| File | Field |
|---|---|
| `pyproject.toml` | `[project].version` |
| `canvas_local_mcp/__init__.py` | `__version__` |
| `server.json` | root `version` **and** `packages[0].version` |
The CI test `test_server_json_version_matches_package` and the workflow validation step will fail if any of these three disagree.
### 2. Merge the PR into `main`
Wait for all CI checks to pass before merging.
### 3. Publish `canvas-local-mcp` to PyPI
The project uses [Trusted Publisher (OIDC)](https://docs.pypi.org/trusted-publishers/) for PyPI uploads.
Trigger the PyPI publish workflow (or run `python -m build && twine upload dist/*` if you have credentials configured) **before** creating the tag, so the package is available when the MCP Registry fetches it.
### 4. Create and push the tag `vX.Y.Z`
```bash
git tag v0.1.4
git push origin v0.1.4
```
This triggers the `Publish to MCP Registry` workflow, which:
1. Validates that `server.json`, `pyproject.toml`, and the tag all declare the same version.
2. Publishes `server.json` to the MCP Registry via GitHub OIDC (no secrets needed).
### 5. Verify
- PyPI: `https://pypi.org/project/canvas-local-mcp/`
- MCP Registry: `https://registry.modelcontextprotocol.io/?q=canvas-mcp`
- GitHub Actions: `https://github.com/admin978/canvas-mcp/actions`
> **Important**: never reuse or move an existing tag. If you need to re-publish after a mistake,
> bump to the next patch version (e.g. `0.1.4`) and start from step 1.
## Contributing, roadmap and support
- [Issues / feature requests](https://github.com/admin978/canvas-mcp/issues)
- Pull requests are welcome for bug fixes and Canvas workflow improvements
- Roadmap direction currently lives in open issues and upcoming PRs
If this project helps you manage Canvas with Claude, please try the flow above and consider giving it a ⭐ so other students and educators can find it.
## License
MIT — see `LICENSE`.
---
Built by [AGENTE 404 S.L.](https://www.agente404.com) · admin@agente404.com
Lo que la gente pregunta sobre canvas-mcp
¿Qué es admin978/canvas-mcp?
+
admin978/canvas-mcp es mcp servers para el ecosistema de Claude AI. Local-first Canvas LMS MCP server. Stdio transport, ~125 lines of Python. Tiene 4 estrellas en GitHub y se actualizó por última vez yesterday.
¿Cómo se instala canvas-mcp?
+
Puedes instalar canvas-mcp clonando el repositorio (https://github.com/admin978/canvas-mcp) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar admin978/canvas-mcp?
+
Nuestro agente de seguridad ha analizado admin978/canvas-mcp y le ha asignado un Trust Score de 87/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene admin978/canvas-mcp?
+
admin978/canvas-mcp es mantenido por admin978. La última actividad registrada en GitHub es de yesterday, con 0 issues abiertos.
¿Hay alternativas a canvas-mcp?
+
Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
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