AI agents call list_subscriptions to retrieve information from Hotmart without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves or queries subscription data without creating, modifying, deleting, or executing any operations. It is a standard read operation that returns information about subscriptions. The low severity reflects minimal blast radius—data exposure is limited to existing subscription records visible to the authenticated user.
From the tool's definition Tool name 'list_subscriptions' and description 'List subscriptions' indicate a query/retrieval operation with no side effects.
Attacks that exploit this kind of access
List subscriptions. It is categorised as a Read tool in the Hotmart MCP Server, which means it retrieves data without modifying state.
Register the Hotmart MCP server in PolicyLayer and add a rule for list_subscriptions: 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 Hotmart. Nothing to install.
list_subscriptions is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_subscriptions 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 list_subscriptions. 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.
list_subscriptions is provided by the Hotmart MCP server (theyahia/hotmart-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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