AI agents use set_slideshow_type to create or update resources in JupyterMCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your JupyterMCP environment.
The tool appears to modify notebook metadata or properties related to slideshow presentation mode. This is a write operation that changes notebook state reversibly (slideshow type can be changed again). It does not execute code, delete data, or trigger financial transactions. Medium severity reflects that misconfiguration could affect notebook presentation but would not cause data loss or code execution risks.
From the tool's definition Tool name 'set_slideshow_type' indicates modification of notebook slideshow configuration; lacks explicit description but sibling tools like 'edit_cell_content', 'insert_and_execute_cell', and 'save_notebook' establish this server's pattern of write…
Documented attack patterns abuse exactly the kind of access set_slideshow_type 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 set_slideshow_type:
{
"version": "1",
"default": "deny",
"tools": {
"set_slideshow_type": {
"limits": [
{
"counter": "set_slideshow_type_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}set_slideshow_type stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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set_slideshow_type. It is categorised as a Write tool in the JupyterMCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Jupyter MCP server in PolicyLayer and add a rule for set_slideshow_type: 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.
set_slideshow_type is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the set_slideshow_type 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 set_slideshow_type. 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.
set_slideshow_type 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.