Ask AI about EXISTING sources in a notebook. NOT for finding new sources.
AI agents call notebook_query to retrieve information from Notebooklm without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves information from already-stored notebook sources. It performs a passive read operation with no side effects—it does not create, modify, delete, execute code, or commit financial obligations. The explicit constraint that it is 'NOT for finding new sources' reinforces that it is limited to querying existing data.
From the tool's definition Tool name 'notebook_query' combined with description 'Ask AI about EXISTING sources in a notebook. NOT for finding new sources.' indicates retrieval of data from existing notebook sources without modification, creation, or deletion.
Documented attack patterns abuse exactly the kind of access notebook_query gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Notebooklm, and nothing reaches the server without passing your rules. This is the rule we recommend for notebook_query:
{
"version": "1",
"default": "deny",
"tools": {
"notebook_query": {}
}
}notebook_query is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Ask AI about EXISTING sources in a notebook. NOT for finding new sources. It is categorised as a Read tool in the Notebooklm MCP Server, which means it retrieves data without modifying state.
Register the Notebooklm MCP server in PolicyLayer and add a rule for notebook_query: 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 Notebooklm. Nothing to install.
notebook_query 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 notebook_query 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 notebook_query. 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.
notebook_query is provided by the Notebooklm MCP server (moodrobotics/notebooklm-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Notebooklm, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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29 Notebooklm tools catalogued and risk-classified — across an index of 43,000+ MCP servers.