answer_choice
Answer a choice activity as the person, with one option (several where the choice allows them) by id from get_choice, as "Save my choice" does. Returns the choice afterwards. Answers as the person, and some choices don't allow changing it: confirm with the person first.
This record as markdown: /tools/falkenslab-moodle-mcp/answer-choice.md
What answer_choice does on Moodle
AI agents use answer_choice to create or update resources in Moodle, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Moodle environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
optionIds | array | Yes | |
activityId | integer | Yes | The activity's id (course module id), as get_course_structure gives it |
Parameters from the server's own tool schema.
Why answer_choice is rated Medium
An AI agent can call answer_choice faster than any human can review: one bad instruction and it creates or modifies resources in Moodle by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs answer_choice 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 answer_choice, this is the rule to start with:
answer_choice 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Moodle, apply this rule, and every answer_choice call is checked against it from then on.
Questions about answer_choice
Answer a choice activity as the person, with one option (several where the choice allows them) by id from get_choice, as "Save my choice" does. Returns the choice afterwards. Answers as the person, and some choices don't allow changing it: confirm with the person first. It is categorised as a Write tool in the Moodle MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
answer_choice accepts 2 parameters: optionIds, activityId. Required: optionIds, activityId. 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 answer_choice: 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.
answer_choice 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 answer_choice 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 answer_choice. 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.
answer_choice 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.
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