Clear all papers from a named in-memory reading list. Useful when restarting a research branch.
Risk signalsBulk/mass operation — affects multiple targets
Part of the ScholarFetch server.
Free to start. No card required.
AI agents may call scholarfetch_saved_clear to permanently remove or destroy resources in ScholarFetch. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call scholarfetch_saved_clear in a loop, permanently destroying resources in ScholarFetch. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"scholarfetch_saved_clear"
]
}See the full ScholarFetch policy for all 12 tools.
These attack patterns abuse exactly the kind of access scholarfetch_saved_clear gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Clear all papers from a named in-memory reading list. Useful when restarting a research branch.. It is categorised as a Destructive tool in the ScholarFetch MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the ScholarFetch MCP server in PolicyLayer and add a rule for scholarfetch_saved_clear: 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 ScholarFetch. Nothing to install.
scholarfetch_saved_clear is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the scholarfetch_saved_clear 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 scholarfetch_saved_clear. 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.
scholarfetch_saved_clear is provided by the ScholarFetch MCP server (https://laibniz-scholarfetch-web.hf.space/mcp/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 12 ScholarFetch tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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