add_feedback_item
Add an item to a feedback activity, after item after (0: first; last without it): a question of any type with its options and settings, a label with its contents, or a page break or a CAPTCHA. Returns its id and the items in order. Changes the course: confirm with the person first.
This record as markdown: /tools/falkenslab-moodle-mcp/add-feedback-item.md
What add_feedback_item does on Moodle
AI agents use add_feedback_item 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 |
|---|---|---|---|
info | string | — | |
type | string | Yes | |
after | integer | — | The item it goes after (0: first) |
label | string | — | |
range | object | — | A numeric item's; a bound left out is none |
fields | object | — | Any other field of the item's form, by name (itemsize, itemmaxlength…) |
display | string | — | checkbox (several answers) for multichoice only |
options | array | — | Choice items' options, in order; given, they replace them all |
contents | string | — | A label's (HTML) |
question | string | — | Every type but a label and a page break needs one |
required | boolean | — | |
dependsOn | object | — | Show it only when another item was answered with this value: an item with a label, on an earlier page (a page break between them) |
Parameters from the server's own tool schema.
Why add_feedback_item is rated Medium
An AI agent can call add_feedback_item 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.
Risk signalsHigh parameter count (22 properties)
Attacks that exploit this kind of access
The rule that runs add_feedback_item 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 add_feedback_item, this is the rule to start with:
add_feedback_item 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 add_feedback_item call is checked against it from then on.
Questions about add_feedback_item
Add an item to a feedback activity, after item after (0: first; last without it): a question of any type with its options and settings, a label with its contents, or a page break or a CAPTCHA. Returns its id and the items in order. Changes the course: 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.
add_feedback_item accepts 12 parameters: info, type, after, label, range, fields, display, options, contents, question, required, dependsOn. Required: type. 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 add_feedback_item: 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.
add_feedback_item 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 add_feedback_item 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 add_feedback_item. 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.
add_feedback_item 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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