AI agents call get_notebook_info to retrieve information from JupyterMCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries metadata and state information about a Jupyter notebook without triggering code execution, modifying cells, or altering the notebook structure. It has no side effects beyond reading. The blast radius if misused is minimal—an agent could only learn about notebook contents, which poses no direct risk compared to execution or destructive tools.
From the tool's definition Tool name 'get_notebook_info' and description 'Get information about the current Jupyter notebook' indicate a retrieval operation with no modification or execution of code.
Documented attack patterns abuse exactly the kind of access get_notebook_info gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and JupyterMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for get_notebook_info:
{
"version": "1",
"default": "deny",
"tools": {
"get_notebook_info": {}
}
} get_notebook_info is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get information about the current Jupyter notebook. It is categorised as a Read tool in the JupyterMCP MCP Server, which means it retrieves data without modifying state.
Register the Jupyter MCP server in PolicyLayer and add a rule for get_notebook_info: 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 JupyterMCP. Nothing to install.
get_notebook_info 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 get_notebook_info 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 get_notebook_info. 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.
get_notebook_info is provided by the Jupyter MCP server (jjsantos01/jupyter-notebook-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from JupyterMCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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11 JupyterMCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.