Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural

Firecrawl Research Index Find the research papers that answer a research query. Some questions have a single answer; many have several — and when in doubt, lean toward returning the fuller relevant set (most relevant first) rather than narrowing to one. A reader is better served seeing the neighboring methods and papers than having them silently dropped. What is in the index Paper abstracts, with full text reachable per paper. The largest share of the corpus is biomedical and life science literature — PubMed journal articles plus bioRxiv and medRxiv preprints — so clinical, drug, gene, disease, epidemiology, and public health questions are in scope. arXiv preprints cover computer science, physics, and mathematics. Coverage outside those sources is thinner: a paper that exists only behind a publisher paywall or in a niche venue may not be indexed, and the general web tools below are the fallback when it isn't. There is no fixed recipe . Read the query, decide what kind it is, and choose the approach below. Some queries need a single search; others need heavy structural/semantic expansion. Don't run machinery a query doesn't call for. The tools, and what each is uniquely good at MCP: firecrawl research search papers(query, k?) CLI: firecrawl research search papers <query [ k <number ] Semantic (HyDE) search over abstracts . The natural first move for almost any query. If results look thin or all alike, re run with a different framing (sibling domain, rival method, dataset/benchmark name) rather than giving up. MCP: firecrawl research related papers(seed ids, intent, mode?, k?) CLI: firecrawl research related papers <seedIds... intent <intent [ mode <similar citers references ] [ k <number ] Semantic and structural expansion, ranked to your intent . This reaches papers semantic search cannot , and it's how you turn one good hit into the rest of a set. mode=similar → niche siblings; citers → who uses/builds on the seeds; references → what they build on / compare against. MCP: firecrawl research inspect paper(id) CLI: firecrawl research inspect paper <id Canonical metadata for one paper: title, abstract, authors, categories, source ids, and dates. Use it after search papers or related papers when you need the complete citation/metadata for a candidate, or when you have an id from elsewhere and need to confirm what paper it resolves to. This does not read the paper body; use read paper for specific full text questions. MCP: firecrawl research read paper(id, question) CLI: firecrawl research read paper <id question <question In body passages of one paper, to verify a load bearing constraint (a method actually used, a score actually reported, an affiliation, what a paper compares to). Use it to settle a specific doubt, not on everything. MCP: firecrawl search(query, categories: ["research"]) CLI: firecrawl search <query categories research Not this index. This is a website filter: it restricts a normal web search to a short list of research affiliated domains — the list does include pubmed.ncbi.nlm.nih.gov , biorxiv.org , medrxiv.org , and arxiv.org alongside publisher sites — and returns page results in a research group beside web , each with url , title , description (the matched passage), position , and category: "research" — web results carry no category , so that is the field to key on when merging. So it reaches those sites' web pages ; what it does not do is query their paper records in this index — no semantic search over abstracts, no citation graph or related paper expansion, no canonical paper metadata, and no in body passages. The results are ordinary web results. Use it when you are already running a web search and want those sites weighed in the same call. For anything that is actually a paper finding task, use firecrawl research search papers and its siblings above. MCP: firecrawl search(query) / firecrawl scrape(url) CLI: firecrawl search <query / firecrawl scrape <url General web search and page fetch, for facts that don't live in paper abstracts: benchmark leaderboards , rankings, "who scores best / is largest / is most used." Find the ranking on the web, then map the top entries back to papers with search papers . Reach for these only when the corpus can't answer the question on its own. Match the approach to the query Single named paper ("the Qwen3 report") → one search papers , done. This is the only case that truly wants exactly one paper. Paper by description / by method or technique ("the paper that introduced X", "training free N gram detection of AI text") → find the best match, then assume there's a family : expand with related papers and include the closely related methods/papers too . Even when one paper is the exact literal match, surface and keep its neighbors — don't narrow to the single best hit and reason the rest out. Only treat it as one answer if the query names a specific paper. Enumeration / method family ("papers that do X", "alternatives to Adam", "benchmarks for Y") → the answer is a set , and this is where related papers earns its keep: expand several strong anchors with mode=similar , re seed from new strong hits. One search is never enough here. Exhibiting ("papers that use / exhibit property P") → the relevant papers apply P but their abstracts may not describe it. Go from P's defining paper outward via citers / references , and use read paper to confirm a candidate actually uses P. Superlative / leaderboard ("best on benchmark X", "largest", "most popular") → the ranking lives on leaderboards / the web , not in any single abstract. Use firecrawl search / firecrawl scrape to find the benchmark's leaderboard or rankings, read off the top models/papers, then search papers each to get its paper. As a fallback, search the benchmark and read paper candidates for reported numbers. The hardest kind — cast wide. Org / author filtered ("from \<org\ ", "by \<author\ ") → topical match isn't enough; verify the affiliation/authorship (metadata or read paper ) before keeping a paper. Compare against ("what does paper X benchmark against / build on") → the answer is inside paper X: read paper(X, ...) or related papers([X], ..., mode="references") . Principles Two different features share the word "research." The paper index is firecrawl research / firecrawl research . The categories: ["research"] option on firecrawl search is a website filter — it does point web search at PubMed, bioRxiv, medRxiv, arXiv, and publisher sites, but what comes back is their web pages, not paper records. If a task is about finding papers, the tools in this skill are the ones that read the corpus; reaching for categories: ["research"] will quietly answer a different question. Query shape and subject field are separate. A clinical trial question and a machine learning question take the same shapes above; what differs is only which source the hits come from. Don't send a biomedical or life science query to the open web on the assumption the corpus is arXiv only — PubMed, bioRxiv, and medRxiv are the largest part of what search papers reads. When in doubt, include. For any topic / method / comparison question, return the relevant family , not just the single best match — err toward keeping a plausibly relevant paper rather than dropping it. The neighboring methods are part of a good answer; don't reason close work out just because one paper is the most exact match. Follow the literature, and keep what you find. The seminal source, the competing methods, the close neighbors are usually a hop away — use related papers , and include them, not just the first hit. Stopping at one good result is the most common way to leave the reader with half an answer. Verify to exclude, not to gatekeep. Use read paper to rule a paper out when a hard constraint clearly fails (wrong org/author, doesn't actually report the score). When a paper is plausibly relevant, lean toward keeping it rather than demanding proof. Only drop the clearly off topic. Don't pad with papers you're confident are unrelated — but that's a high bar; most plausibly relevant work should make the cut. See also [firecrawl build search](https://github.com/firecrawl/skills/tree/main/skills/build/firecrawl build search) — building the paper index into an app instead of querying it here

firecrawl-research-index