Get recommendations using multiple positive and negative example papers.
AI agents call semantic_scholar_multi_recommend to retrieve information from Semantic Scholar MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries the Semantic Scholar database to retrieve recommendations based on provided positive and negative examples. It has no side effects—it does not create, modify, delete, or execute any irreversible operations. The operation is purely informational retrieval, making it a Read-category tool with low severity risk.
From the tool's definition Tool name and description indicate retrieval of paper recommendations based on example inputs. The description states 'Get recommendations' which is a query operation. No modifications, deletions, or external actions are mentioned.
Documented attack patterns abuse exactly the kind of access semantic_scholar_multi_recommend gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Semantic Scholar MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for semantic_scholar_multi_recommend:
{
"version": "1",
"default": "deny",
"tools": {
"semantic_scholar_multi_recommend": {}
}
}semantic_scholar_multi_recommend is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get recommendations using multiple positive and negative example papers. It is categorised as a Read tool in the Semantic Scholar MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Semantic Scholar MCP Server MCP server in PolicyLayer and add a rule for semantic_scholar_multi_recommend: 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 Semantic Scholar MCP Server. Nothing to install.
semantic_scholar_multi_recommend 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 semantic_scholar_multi_recommend 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 semantic_scholar_multi_recommend. 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.
semantic_scholar_multi_recommend is provided by the Semantic Scholar MCP Server MCP server (smaniches/semantic-scholar-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Semantic Scholar MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
14 Semantic Scholar MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.