set_database_entry_approved
Approve a database entry waiting for approval, or take its approval back: one waiting shows only to its author and to who approves. Its author sees it: confirm with the person first.
This record as markdown: /tools/falkenslab-moodle-mcp/set-database-entry-approved.md
What set_database_entry_approved does on Moodle
AI agents use set_database_entry_approved 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 |
|---|---|---|---|
entryId | integer | Yes | The entry's id, as list_database_entries gives it |
approved | boolean | Yes | |
databaseId | integer | Yes | The database's id (course module id), as get_course_structure gives it |
Parameters from the server's own tool schema.
Why set_database_entry_approved is rated Medium
An AI agent can call set_database_entry_approved 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 set_database_entry_approved 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 set_database_entry_approved, this is the rule to start with:
set_database_entry_approved 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 set_database_entry_approved call is checked against it from then on.
Questions about set_database_entry_approved
Approve a database entry waiting for approval, or take its approval back: one waiting shows only to its author and to who approves. Its author sees 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.
set_database_entry_approved accepts 3 parameters: entryId, approved, databaseId. Required: entryId, approved, databaseId. 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 set_database_entry_approved: 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.
set_database_entry_approved 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 set_database_entry_approved 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 set_database_entry_approved. 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.
set_database_entry_approved 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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