Get the daily curated AI/ML trending papers from Semantic Scholar, ranked by citation count. Five fan-out queries (large language model, transformer, RLHF, AI agents, diffusion model), deduped by paperId, top 30 returned. Each entry carries paperId, title, abstract, authors, year, venue, citation...
AI agents call get_ai_trending_papers to retrieve information from TensorFeed without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
limit | number | — | Max papers to return (1-30). Default 15. |
Parameters from the server's own tool schema.
This is a straightforward data retrieval operation. It fetches structured information about academic papers and ranks them—classic read-only query behavior. No side effects, no code execution, no data modification or deletion. The 'five fan-out queries' are internal search operations to populate results, not external executions.
From the tool's definition Tool retrieves and queries trending papers from Semantic Scholar with metadata (paperId, title, abstract, authors, etc.). Returns data without creating, modifying, deleting, or executing external operations.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
Get the daily curated AI/ML trending papers from Semantic Scholar, ranked by citation count. Five fan-out queries (large language model, transformer, RLHF, AI agents, diffusion model), deduped by paperId, top 30 returned. Each entry carries paperId, title, abstract, authors, year, venue, citationCount, arxivId, doi, and fieldsOfStudy. Refreshed daily at 11:00 UTC. Citation-ranked counterpart to get_arxiv_recent (firehose by submission date). License: Semantic Scholar API permits use; the standard attribution block ships on every response. It is categorised as a Read tool in the TensorFeed MCP Server, which means it retrieves data without modifying state.
get_ai_trending_papers accepts 1 parameter: limit. The full parameter table on this page comes from the server's own tool schema.
Register the TensorFeed MCP server in PolicyLayer and add a rule for get_ai_trending_papers: 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 TensorFeed. Nothing to install.
get_ai_trending_papers 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 get_ai_trending_papers 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 get_ai_trending_papers. 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.
get_ai_trending_papers is provided by the TensorFeed MCP server (https://mcp.tensorfeed.ai/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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