AI agents call check_dependencies to retrieve information from notebookLM2PPT without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The name 'check_dependencies' strongly implies a read-only verification operation that checks whether required software dependencies are installed or available. No description is provided to confirm or deny this. Given the server context (PDF to PPT conversion), this likely checks for required libraries. Empty description lowers confidence significantly.
From the tool's definition Tool name 'check_dependencies' with empty description. Name suggests checking/verifying installed dependencies rather than modifying anything.
Documented attack patterns abuse exactly the kind of access check_dependencies gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and notebookLM2PPT, and nothing reaches the server without passing your rules. This is the rule we recommend for check_dependencies:
{
"version": "1",
"default": "deny",
"tools": {
"check_dependencies": {}
}
}check_dependencies is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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check_dependencies. It is categorised as a Read tool in the notebookLM2PPT MCP Server, which means it retrieves data without modifying state.
Register the notebookLM2PPT MCP server in PolicyLayer and add a rule for check_dependencies: 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 notebookLM2PPT. Nothing to install.
check_dependencies 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 check_dependencies 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 check_dependencies. 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.
check_dependencies is provided by the notebookLM2PPT MCP server (neosun100/notebooklm2ppt). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from notebookLM2PPT, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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3 notebookLM2PPT tools catalogued and risk-classified — across an index of 43,000+ MCP servers.