Proporciona feedback sobre una tarea entregada por un estudiante
AI agents use provide_feedback to create or update resources in Moodle MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Moodle MCP Server environment.
This tool writes/creates feedback on a student's submission in Moodle. It modifies data (adds or updates feedback) but is reversible as feedback can typically be edited or removed. It does not delete data or execute code, placing it in the Write category.
From the tool's definition 'Proporciona feedback sobre una tarea entregada por un estudiante' (Provides feedback on a task submitted by a student)
Documented attack patterns abuse exactly the kind of access provide_feedback gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Moodle MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for provide_feedback:
{
"version": "1",
"default": "deny",
"tools": {
"provide_feedback": {
"limits": [
{
"counter": "provide_feedback_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}provide_feedback stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Proporciona feedback sobre una tarea entregada por un estudiante. It is categorised as a Write tool in the Moodle MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Moodle MCP Server MCP server in PolicyLayer and add a rule for provide_feedback: 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 Moodle MCP Server. Nothing to install.
provide_feedback is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the provide_feedback 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 provide_feedback. 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.
provide_feedback is provided by the Moodle MCP Server MCP server (peancor/moodle-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 8 Moodle MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
8 Moodle MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.