Add one paper to a named in-memory reading list on the MCP server. Best input is paper_json copied from another ScholarFetch tool result, but DOI, query+result_index, or author_name+candidate_index+paper_index also work. Reuse the same collection name across calls to keep one research session tog...
Risk signalsAccepts freeform code/query input (query)
Part of the ScholarFetch server.
Free to start. No card required.
AI agents use scholarfetch_saved_add to create or modify resources in ScholarFetch. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call scholarfetch_saved_add repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach ScholarFetch.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"scholarfetch_saved_add": {
"limits": [
{
"counter": "scholarfetch_saved_add_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full ScholarFetch policy for all 12 tools.
These attack patterns abuse exactly the kind of access scholarfetch_saved_add gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Add one paper to a named in-memory reading list on the MCP server. Best input is paper_json copied from another ScholarFetch tool result, but DOI, query+result_index, or author_name+candidate_index+paper_index also work. Reuse the same collection name across calls to keep one research session together.. It is categorised as a Write tool in the ScholarFetch MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the ScholarFetch MCP server in PolicyLayer and add a rule for scholarfetch_saved_add: 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_add 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 scholarfetch_saved_add 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_add. 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_add 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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