Download a generated audio artifact to a local file.
AI agents call artifact_download_audio to retrieve information from NotebookLM MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves an already-generated audio artifact and saves it locally. It reads/fetches existing data without modifying, creating, or deleting anything on the server side. The only side effect is writing a local file, which is a low-risk read/export operation.
From the tool's definition Download a generated audio artifact to a local file
Attacks that exploit this kind of access
Download a generated audio artifact to a local file. It is categorised as a Read tool in the NotebookLM MCP Server MCP Server, which means it retrieves data without modifying state.
Register the NotebookLM MCP Server MCP server in PolicyLayer and add a rule for artifact_download_audio: 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 MCP Server. Nothing to install.
artifact_download_audio 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 artifact_download_audio 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 artifact_download_audio. 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.
artifact_download_audio is provided by the NotebookLM MCP Server MCP server (pavelguzenfeld/notebooklm-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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