| name | canvas-lms-mcp-connector |
| description | MCP connector for Canvas LMS – enables AI-powered course management, assignment distribution, and learner analytics through Claude |
| license | AGPL |
| tags | ["education","lms","mcp","canvas","elearning"] |
| difficulty | intermediate |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Canvas Lms MCP Connector
Overview
This skill enables Claude to interact with Canvas Lms through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Canvas Lms's REST API, allowing natural language control of Canvas Lms operations, intelligent automation, and AI-powered assistance for Canvas Lms workflows.
When to Use This Skill
- Course and module creation via natural language
- Assignment and rubric generation
- Grade book analysis and student progress tracking
- Calendar and scheduling management
- Discussion board facilitation and summarization
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Canvas Lms │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Canvas Lms operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/api/v1/courses, /api/v1/courses/:id/assignments, /api/v1/courses/:id/modules, /api/v1/courses/:id/enrollments
Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const server = new McpServer({
name: "canvas-lms-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Canvas Lms resources with optional filters",
{
query: z.string().optional().describe("Search query or filter"),
limit: z.number().optional().describe("Max results to return"),
},
async ({ query, limit }) => {
const response = await fetch(`${BASE_URL}/api/v1/courses`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(transport);
Claude Desktop Configuration
{
"mcpServers": {
"canvas-lms-mcp-connector": {
"command": "node",
"args": ["path/to/canvas-lms-mcp-connector/index.js"],
"env": {
"CANVAS_LMS_API_KEY": "your-api-key",
"CANVAS_LMS_BASE_URL": "https://your-instance-url"
}
}
}
}
Best Practices
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Canvas Lms API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Canvas Lms API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Create a module with 3 assignments, progressive due dates, and peer review enabled"
Security Considerations
- All API credentials must be stored as environment variables
- Implement input validation and sanitization for all tool parameters
- Use HTTPS for all API communications
- Follow the principle of least privilege for API token permissions
- Audit log all write operations for compliance tracking
Resources
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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