AI agents call get_assignment to retrieve information from Canvas MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves assignment information without modifying, deleting, or executing any operations. It is purely informational, consistent with other Read category tools on the server like 'get_course', 'get_file', and 'get_current_user'. The blast radius of misuse is minimal—an AI agent could only access assignment details it may not be authorized to see, not cause irreversible changes or financial impact.
From the tool's definition Tool name 'get_assignment' and description 'Get details about a specific assignment' indicate a retrieval operation. The verb 'Get' and the absence of language suggesting modification, deletion, or execution confirm this is a read-only query.
Documented attack patterns abuse exactly the kind of access get_assignment gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Canvas MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_assignment:
{
"version": "1",
"default": "deny",
"tools": {
"get_assignment": {}
}
}get_assignment is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get details about a specific assignment. It is categorised as a Read tool in the Canvas MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Canvas MCP Server MCP server in PolicyLayer and add a rule for get_assignment: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Canvas MCP Server. Nothing to install.
get_assignment is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_assignment rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for get_assignment. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
get_assignment is provided by the Canvas MCP Server MCP server (plyght/canvas-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Canvas MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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30 Canvas MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.