Get all assignments for a specific Gradescope course.
AI agents call tool_get_assignments to retrieve information from Gradescope 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 or queries assignment data from a Gradescope course with no side effects. It does not create, modify, delete, or execute any operations—it simply returns existing assignment information. This is a standard Read category operation, with low severity since querying course assignments carries minimal risk even if misused by an AI agent.
From the tool's definition The tool is named 'tool_get_assignments' and described as 'Get all assignments for a specific Gradescope course.' The verb 'Get' and the action of retrieving assignment data without modification indicate a read-only operation.
Documented attack patterns abuse exactly the kind of access tool_get_assignments gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Gradescope MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for tool_get_assignments:
{
"version": "1",
"default": "deny",
"tools": {
"tool_get_assignments": {}
}
}tool_get_assignments is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get all assignments for a specific Gradescope course. It is categorised as a Read tool in the Gradescope MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Gradescope MCP Server MCP server in PolicyLayer and add a rule for tool_get_assignments: 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 Gradescope MCP Server. Nothing to install.
tool_get_assignments 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 tool_get_assignments 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 tool_get_assignments. 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.
tool_get_assignments is provided by the Gradescope MCP Server MCP server (yuanpeng-li/gradescope-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Gradescope 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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37 Gradescope MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.