update_bundle_enrollment

Update a bundle enrollment (e.g. change expiry, free trial status).

SERVERThinkific MCP Server SOURCEackbarguppi-ai/thinkific-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/ackbarguppi-ai-thinkific-mcp/update-bundle-enrollment.md

WHAT IT DOES

What update_bundle_enrollment does on Thinkific MCP Server

AI agents use update_bundle_enrollment to create or update resources in Thinkific MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Thinkific MCP Server environment.

RISK

Why update_bundle_enrollment is rated Medium

The tool modifies enrollment data (expiry dates, trial status) but does not delete data or execute arbitrary code. Changes are reversible through subsequent updates. Severity is medium because incorrect modifications could affect student access and course availability, but the blast radius is limited to individual enrollment records rather than system-wide or financial impacts.

From the tool's definition Tool description states 'Update a bundle enrollment' with examples of modifying 'expiry, free trial status' - these are reversible modifications to existing enrollment records.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs update_bundle_enrollment safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Thinkific MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For update_bundle_enrollment, this is the rule to start with:

update_bundle_enrollment Rate-limited

update_bundle_enrollment 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "update_bundle_enrollment": {
      "limits": [
        {
          "counter": "update_bundle_enrollment_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Thinkific MCP Server, apply this rule, and every update_bundle_enrollment call is checked against it from then on.

FAQ

Questions about update_bundle_enrollment

What does the update_bundle_enrollment tool do? +

Update a bundle enrollment (e.g. change expiry, free trial status). It is categorised as a Write tool in the Thinkific MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on update_bundle_enrollment? +

Register the Thinkific MCP Server MCP server in PolicyLayer and add a rule for update_bundle_enrollment: 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 Thinkific MCP Server. Nothing to install.

What risk level is update_bundle_enrollment? +

update_bundle_enrollment is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit update_bundle_enrollment? +

Yes. Add a rate_limit block to the update_bundle_enrollment 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.

How do I block update_bundle_enrollment completely? +

Set action: deny in the PolicyLayer policy for update_bundle_enrollment. 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.

What MCP server provides update_bundle_enrollment? +

update_bundle_enrollment is provided by the Thinkific MCP Server MCP server (ackbarguppi-ai/thinkific-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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