AI agents call paper_relevance_search 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 retrieves or queries academic paper data based on relevance criteria. It has no side effects—it does not create, modify, delete, or execute code. The empty description slightly reduces confidence, but the naming pattern and server context strongly indicate this is a read-only search function consistent with other tools on the Semantic Scholar API server.
From the tool's definition Tool name 'paper_relevance_search' combined with server context (Semantic Scholar academic data API) and sibling tools (author_search, paper_autocomplete, paper_batch_details) all indicate query/retrieval operations.
Documented attack patterns abuse exactly the kind of access paper_relevance_search 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 paper_relevance_search:
{
"version": "1",
"default": "deny",
"tools": {
"paper_relevance_search": {}
}
}paper_relevance_search is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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paper_relevance_search. 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 paper_relevance_search: 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.
paper_relevance_search 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 paper_relevance_search 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 paper_relevance_search. 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.
paper_relevance_search is provided by the Semantic Scholar MCP Server MCP server (zongmin-yu/semantic-scholar-fastmcp-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 16 Semantic Scholar MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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16 Semantic Scholar MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.