Quick query to Gemini model with optional grounding tools. > ⚠️ REQUIRES GEMINI_API_KEY - Do NOT use this tool unless GEMINI_API_KEY is configured. For notebook queries, use ask_question instead (no API key needed). Supports: - Google Search grounding for current information - Code execution for ...
Part of the Notebooklm server.
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AI agents call gemini_query to retrieve information from Notebooklm without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though gemini_query only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"gemini_query": {}
}
}See the full Notebooklm policy for all 68 tools.
These attack patterns abuse exactly the kind of access gemini_query gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Quick query to Gemini model with optional grounding tools. > ⚠️ REQUIRES GEMINI_API_KEY - Do NOT use this tool unless GEMINI_API_KEY is configured. For notebook queries, use ask_question instead (no API key needed). Supports: - Google Search grounding for current information - Code execution for calculations - URL analysis for web content Requirements - GEMINI_API_KEY environment variable MUST be set When to Use (ONLY if API key is configured) - Quick factual questions NOT in your notebooks - Current events (with google_search tool) - Code calculations (with code_execution tool) - Web page analysis (with url_context tool) When NOT to Use - Use ask_question instead for queries about your NotebookLM notebooks (no API key needed). 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 gemini_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.
gemini_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 gemini_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 gemini_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.
gemini_query is provided by the Notebooklm MCP server (Pantheon-Security/notebooklm-mcp-secure). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 68 Notebooklm tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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