get_choice_results
Read a choice's responses as its report shows a teacher: each option with how many chose it and who (aliases when the server pseudonymizes), and who hasn't answered where the choice shows that. The report names people whatever the choice publishes to students.
This record as markdown: /tools/falkenslab-moodle-mcp/get-choice-results.md
What get_choice_results does on Moodle
AI agents call get_choice_results to retrieve information from Moodle without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
choiceId | integer | Yes | The activity's id (course module id), as get_course_structure gives it |
Parameters from the server's own tool schema.
Why get_choice_results is rated Low
Even though get_choice_results only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs get_choice_results safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Moodle, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For get_choice_results, this is the rule to start with:
get_choice_results is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Moodle, apply this rule, and every get_choice_results call is checked against it from then on.
Questions about get_choice_results
Read a choice's responses as its report shows a teacher: each option with how many chose it and who (aliases when the server pseudonymizes), and who hasn't answered where the choice shows that. The report names people whatever the choice publishes to students. It is categorised as a Read tool in the Moodle MCP Server, which means it retrieves data without modifying state.
get_choice_results accepts 1 parameter: choiceId. Required: choiceId. The full parameter table on this page comes from the server's own tool schema.
Register the Moodle MCP server in PolicyLayer and add a rule for get_choice_results: 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. Nothing to install.
get_choice_results 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_choice_results 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_choice_results. 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_choice_results is provided by the Moodle MCP server (@falkenslab/moodle-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on Moodle, and thousands of servers like it.
This server
Across the catalogue