ipynb_search_notebooks
AI agents call ipynb_search_notebooks to retrieve information from Jupyter Editor without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Despite the empty description, the naming convention ('search') and context of sibling tools strongly suggest this searches notebook metadata or content for retrieval only, with no side effects. No modifications, deletions, or external execution are implied. Confidence is slightly reduced due to lack of explicit description, but the pattern fits Read category unambiguously.
From the tool's definition Tool name 'ipynb_search_notebooks' with 'search' verb, typical of read operations. Server description emphasizes 'reading, modifying, and batch-processing notebooks.' Sibling tools include clear Read operations (get_cell, get_notebook_info, filter_cells,…
Attacks that exploit this kind of access
ipynb_search_notebooks. It is categorised as a Read tool in the Jupyter Editor MCP Server, which means it retrieves data without modifying state.
Register the Jupyter Editor MCP server in PolicyLayer and add a rule for ipynb_search_notebooks: 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 Jupyter Editor. Nothing to install.
ipynb_search_notebooks 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 ipynb_search_notebooks 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 ipynb_search_notebooks. 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.
ipynb_search_notebooks is provided by the Jupyter Editor MCP server (jsamuel1/jupyter-editor-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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