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search finds peer-reviewed papers for a query and returns them ranked, in one call. Your assistant reads the papers and writes the answer. It’s the right tool for lookups and for questions your assistant can answer from a list of papers. For a question that needs several research steps and a written, cited answer, use create_thread instead. See Search vs Threads. search works without an account at reduced limits. ChatGPT Deep Research may call it several times with different queries and filters while it builds a report.

Parameters

Every parameter except query is optional.

Response

search returns a text payload written for AI agents: the ranked papers (title, link, authors, year, citation count, journal, and abstract), plus instructions to cite each paper inline by its number.
Paid plans also get DOIs and, with include_full_text_chunks, full-text excerpts. In ChatGPT, results appear in the Consensus widget.
Need structured JSON? Use the Consensus API, which shares your monthly allowance with MCP.

Example prompts

  • “What does the research say about the effectiveness of remote work on productivity?”
  • “Find RCTs and meta-analyses since 2020 on cognitive behavioral therapy for anxiety”
  • “Search for high-quality human studies on gut microbiome and mental health with at least 100 participants”
  • “Recent research on large language model hallucination from top-tier journals”

Good to know

  • Cost: one call per 100 papers returned, rounded up, with a minimum of one call per search. See plans and access.
  • Papers per search: your plan sets the maximum page_size, from 3 without an account up to 1,000 on Enterprise.
  • Rate limits: set by your plan. See plans and access.