Loads a .ipynb file into memory. Prepares notebook for efficient, cost-effective text-based operations with LLMs.
AI agents call load_notebook to retrieve information from Notebookllm without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and reads notebook data from disk into memory for processing. It performs no creation, modification, deletion, execution, or financial operations. While it could theoretically expose sensitive data if notebooks contain secrets, the act itself is a pure read operation with minimal blast radius if misused by an agent—at worst it loads an unintended file, but causes no side effects to data state.
From the tool's definition Tool description states it 'Loads a .ipynb file into memory' and 'Prepares notebook for efficient, cost-effective text-based operations'.
Documented attack patterns abuse exactly the kind of access load_notebook gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Notebookllm, and nothing reaches the server without passing your rules. This is the rule we recommend for load_notebook:
{
"version": "1",
"default": "deny",
"tools": {
"load_notebook": {}
}
}load_notebook is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Loads a .ipynb file into memory. Prepares notebook for efficient, cost-effective text-based operations with LLMs. It is categorised as a Read tool in the Notebookllm MCP Server, which means it retrieves data without modifying state.
Register the Notebookllm MCP server in PolicyLayer and add a rule for load_notebook: 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 Notebookllm. Nothing to install.
load_notebook 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 load_notebook 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 load_notebook. 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.
load_notebook is provided by the Notebookllm MCP server (yasirrazaa/notebookllm_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Notebookllm, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
6 Notebookllm tools catalogued and risk-classified — across an index of 43,000+ MCP servers.