assess_workshop_example
Assess an example submission through the workshop's assessment form, as assess_workshop_submission does a submission: a teacher's assessment is the reference participants' are compared with; a participant's is their practice, where the workshop lets them assess examples now. Returns the grade it ...
This record as markdown: /tools/falkenslab-moodle-mcp/assess-workshop-example.md
What assess_workshop_example does on Moodle
AI agents use assess_workshop_example 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 |
|---|---|---|---|
fields | object | — | Any other field of the form, by name |
criteria | array | — | In the form's order |
feedback | string | — | Overall feedback for the author (HTML); no aliases in it |
exampleId | integer | Yes | As list_workshop_examples gives it |
workshopId | integer | Yes | The workshop's id (course module id) |
Parameters from the server's own tool schema.
Why assess_workshop_example is rated Medium
An AI agent can call assess_workshop_example 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 assess_workshop_example 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 assess_workshop_example, this is the rule to start with:
assess_workshop_example 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 assess_workshop_example call is checked against it from then on.
Questions about assess_workshop_example
Assess an example submission through the workshop's assessment form, as assess_workshop_submission does a submission: a teacher's assessment is the reference participants' are compared with; a participant's is their practice, where the workshop lets them assess examples now. Returns the grade it gives. 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.
assess_workshop_example accepts 5 parameters: fields, criteria, feedback, exampleId, workshopId. Required: exampleId, workshopId. 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 assess_workshop_example: 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.
assess_workshop_example 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 assess_workshop_example 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 assess_workshop_example. 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.
assess_workshop_example 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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