Scite
Search scientific literature
- Category
- Data & Analytics
- Primary Subcategory
- Academic Literature Search & Research Assistants
Integration details
Description
scite helps users search and read scientific papers, inspect citation context and citation graphs, format bibliographies, record citation decisions, and organize papers and notes in Collections through ChatGPT.
- Integration type
- Plugin
- Verification status
- Not applicable
- Platform
- ChatGPT
- Primary Subcategory
- Academic Literature Search & Research Assistants
- Secondary Subcategories
- None listed
- Brand
- Scite
- Access
- Account required
- First tracked
- 2026-08-14
- Tool count
- 35
- Geography
- US
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Competing in ChatGPT Academic Literature Search & Research Assistants
View Category35 tools agents can invoke
Add DOIs to a Collection. Works on both DOI-list and saved-search Collections. Requires EDITOR or ADMIN access. For a DOI-list Collection the DOIs are added to the list. For a saved-search Collection they are force-included (added to the manual include list) so they appear even if the search would not return them. DOIs already present are ignored. Use `create_collection` to make a new Collection or `remove_dois_from_collection` to take DOIs out. **Parameters:** - slug: The Collection slug (required). - dois: List of DOI strings to add (required, non-empty). **Returns:** The updated Collection with id, slug, name, and DOI counts.
add_dois_to_collection
Format a set of DOIs as a ready-to-import reference list (bibliography). Give it the DOIs of papers you have already found (e.g. via `search_literature`) and it returns a single formatted reference list in the requested citation format, built from Scite's stored metadata (authors, title, journal, year, volume, issue, page). Use this instead of hand-writing BibTeX/RIS — the output is machine-formatted for direct import into reference managers (Zotero, EndNote, Mendeley) or a manuscript's bibliography, so keys and fields are exact. **Parameters:** - dois: List of DOI strings to include (required, up to 500). Order and de-duplication are preserved. - format: 'bibtex' (default), 'ris', or 'csv'. **Returns:** JSON with `format`, `found` (count), `notFound` (DOIs with no record), and `content` (the formatted reference list as text).
bibliography
Traverse the scite citation graph from seed DOIs to discover connected papers by citation topology rather than keyword. Each edge is a citation: `s` (citing paper) -> `t` (cited paper). Use it to find prior art / foundations (`direction="out"`), impact and follow-on work (`direction="in"`), or both. Get seed DOIs from `search_literature` first, then traverse; feed interesting nodes back into `search_literature` or `read_fulltext` for content. This tool returns structure (edges + titles), not abstracts or full text. Seeds are automatically expanded to their linked preprint/published versions (e.g. an arXiv DOI and its journal DOI), so a paper split across two DOIs is traversed as one work with a merged citation pool; the extra DOIs appear in `seeds` and `seed_coverage`. Response: `edges` is a deduped list of `{"s","t","d"}` (d = hop distance from a seed); `papers` maps every DOI in the graph to its `{title, year}`. `truncated` is true when the `max_edges` cap was hit. **Coverage — check before trusting the topology.** `seed_coverage` maps each seed to how many of its edges resolved directly (hop 1); `low_coverage_seeds` lists seeds with too few to be reliable. Coverage is per-paper: it only follows references/citers scite resolved to DOIs, and arXiv-heavy (e.g. ML) papers often under-resolve, so a thin graph means missing edges, not a poorly-connected paper. For any seed in `low_coverage_seeds`, treat its topology as incomplete and fall back to `search_literature` for that paper. **Citation intent (scite's differentiator).** Set `include_intent=true` to annotate each edge with the smart-citation `type` (supporting / contrasting / mentioning) and the `section` it appears in — this turns the structure into an *intent graph*: you can see agreement vs. dissent, and method vs. background citations. Set `include_snippets=true` to additionally attach up to 3 `snippets` of the actual citing text (implies intent). Snippet text is returned only for open-access / unrestricted sources; closed-publisher citations still carry type and section. **Custom "why was it cited?" classifications from `snippets`.** scite's `type` is a fixed 3-way label (supporting / contrasting / mentioning). When you need a finer or domain-specific taxonomy — e.g. *uses as baseline*, *extends the method*, *reuses the dataset*, *critiques an assumption*, *motivates the problem* — read the `snippets` (the verbatim sentence the citing paper used) and classify each edge yourself into whatever scheme the task calls for. The snippet is the ground truth; treat scite's `type`/`section` as a prior, not the final answer. Group edges by your derived label to answer "why does this literature cite X?". When a snippet is too short to judge the intent, chain `read_fulltext` on the citing paper (edge `s`) to read the surrounding paragraph / section and recover the full rationale — snippets are ~1 sentence each, `read_fulltext` gives you the argument around them. Typical flow: `citation_graph` (structure + snippets) -> pick the edges that matter -> `read_fulltext(dois=[citing paper])` with a targeted `term` to confirm *why* it was cited before you label it. **Recording a screen.** When you use this tool to screen literature — keeping some papers and dropping others with a reason — record those keep/drop decisions with `report_citations` (cited / excluded + reason_code), and `citation_report` will summarize the include/exclude funnel as a PRISMA-style audit. **Deriving analyses from `edges` (no extra calls).** The edge list is all you need for the classic citation-analysis questions — compute them directly: - *Common references* (shared foundations): traverse `direction="out"` from multiple seeds; take the DOIs that appear as `t` for every seed. - *Common citers* (who cites all of them, e.g. surveys/syntheses): `direction="in"`; DOIs appearing as `s` for every seed. - *Bibliographic coupling* (papers similar to a seed): `direction="out"` at depth 2; rank other papers by how many `t` references they share with the seed. - *Co-citation* (papers cited alongside a seed): `direction="in"` then `out`; rank papers frequently cited by the same citers. - *Citation classics* (most influential in the network): rank DOIs by in-degree (how often they appear as `t`). Filter any of these to supporting/contrasting edges by adding `include_intent=true` first.
citation_graph
Generate a report of the sources you included and excluded in this session, with reasons — for fact-checking, provenance, and systematic-review/regulatory audit. Summarizes the citation decisions recorded via report_citations for the current session (or a specific answer_id): how many sources were retrieved, screened, included (cited), and excluded, the breakdown of exclusion reasons and screening stages, provenance by source, and the full per-source lists — so the user can review and sanity-check the reasoning behind each include/exclude choice. Use it for: - Fact-checking: show which sources were used vs rejected and why, so unsupported or hallucinated claims stand out; `checks` flags decisions missing a reason. - Provenance transparency: `by_source` and each item's `source` show which citations came from scite retrieval vs web/user-supplied, so a reader can weigh how verified each source is. - Systematic review / PRISMA: the identified -> screened -> included/excluded funnel with per-reason and per-stage counts is a PRISMA-compliant screening record. - Regulatory / audit: a reproducible, per-source account of the include/exclude reasoning for an evidence submission or compliance review. Call report_citations first to record the decisions; pass the same answer_id here to scope the report to one answer. Read-only — it does not change anything. Returns JSON: summary (retrieved / screened / included / excluded counts; retrieved is null for an answer-scoped report), excluded_by_reason, by_stage, by_source, checks (e.g. decisions missing a reason), truncated, and the full included[]/excluded[] lists.
citation_report
Create a new Collection owned by the signed-in user. Use this to start a Collection from a list of DOIs the user wants to group, track, and analyze together. The caller becomes the Collection ADMIN. The returned `slug` identifies the Collection for `get_collection`, `update_collection`, `add_dois_to_collection`, and the other Collection tools. **DOI validation.** Provided DOIs are validated and resolved against scite; unknown DOIs are dropped and surfaced via the `unmatchedDoiCount` in the response. An empty `dois` list creates an empty Collection the user can add to later. **Scope.** This tool creates DOI-list Collections. Collections backed by a saved search query are created in the scite web app, not via MCP. **Parameters:** - name: Collection name (required). - description: Optional free-text description. - dois: Optional list of DOI strings to seed the Collection. - is_public: If true, anyone with the slug can view the Collection (default: false). **Returns:** The created Collection with id, slug, name, description, isPublic, doiQueryType, accessType, and DOI counts.
create_collection
Write a note on a Collection, attributed to the signed-in user. Use this to persist something the user wants to keep with a Collection: a synthesis of a conversation, a reading plan, why a paper matters, what to check next. The note is stored in scite and is visible to everyone the Collection is shared with, in the web app as well as here — so write it for a human reader, not as a scratchpad. Markdown is the default body format and is rendered as Markdown in the scite UI. Pass `body_format: "PLAIN"` for text that should not be interpreted as Markdown. **Titles.** Omit `title` and one is derived from the first line of the body, so a quick note needs no naming. Supply a title when the user names the note or when the body's first line reads poorly as a heading. **Access.** The caller must be a member of the Collection (its owner, shared on it, or in the organization it is shared with). Body limit 20,000 characters; title limit 500. **Parameters:** - slug: The Collection slug (required) — from `create_collection` or `search_collections`. - body: The note text, Markdown by default. Optional only when you supply a title. - title: Optional heading; derived from the body when omitted. - body_format: "MARKDOWN" (default) or "PLAIN". Send a body, a title, or both — a note with neither is rejected. **Returns:** The created note with id, title, body, bodyFormat, createdBy, and timestamps.
create_collection_note
Permanently delete a Collection. Requires ADMIN access on the Collection. This cannot be undone. The Collection and its DOI membership are removed. Only the Collection ADMIN may delete it. **Parameters:** - slug: The Collection slug (required). **Returns:** `{deleted: true, slug: "..."}` on success.
delete_collection
Delete a note from a Collection. Only the note's author may delete it. The note stops appearing for everyone on the Collection. Confirm with the user before calling this — notes are written by hand and there is no undo exposed here. Deleting a teammate's note is not allowed. To take a paper out of a Collection use `remove_dois_from_collection`; to delete the Collection itself use `delete_collection`. **Parameters:** - slug: The Collection slug (required). - note_id: The note's numeric id (required) — from `list_collection_notes`. **Returns:** `{deleted: true, noteId: N, slug: "..."}` on success.
delete_collection_note
Fetch the full text of a single FDA 510(k) summary PDF by document ID. Use this after `search_510k_summaries` or `search_device510k` when you need the complete narrative text of a 510(k) summary, not just search snippets or structured metadata. Returns the full extracted text organized by page. **Parameters:** - id: Document identifier (the K number, e.g. `K192757`). Can be obtained from either `search_510k_summaries` or `search_device510k` results. **Returns:** The full-text content of the 510(k) summary PDF, organized by page, with file metadata and ontology tags.
get_510k_summary
Fetch full details for a single clinical trial by NCT id. Use this after `search_clinical_trials` when you need the complete record for a specific trial, including the full `description`, study `design`, `enrollment`, `outcomes` (primary/secondary), full `eligibility` inclusion/exclusion criteria, `reportedEvents` (adverse events when the trial has posted results), principal investigator (`pi`), `contacts`, `citations` (related publications), and `resultsUrl`. The search tool returns a slim summary to save tokens; call this tool for a specific NCT id when you need those verbose fields for deeper analysis or patient-trial matching. **Parameters:** - id: NCT identifier (e.g. `NCT02986230`). **Returns:** A compact detail record preserving all trial fields except the low-signal `ontology` classifications.
get_clinical_trial
Fetch a single Collection (a saved, named set of papers) by its slug. Use the `slug` returned by `create_collection` or `search_collections`. Returns the Collection's identity, sharing, access level, and DOI counts. The caller must have at least VIEWER access (own it, be shared on it, or it is public). **Notes.** `noteCount` reports how many notes the Collection holds — free-text Markdown the team has written on it. Pass `include_notes: true` to also get an index of up to 25 of them (id, title, author, timestamps, no bodies); `notesTruncated: true` means there are more than the index shows. Use `list_collection_notes` to read note bodies, and `create_collection_note` to add one. Notes are team-internal: `noteCount` and the index are only returned to members of the Collection, so they are absent for a public Collection the caller merely has access to read. **Papers.** This tool returns DOI *counts*, not the DOI list. To see or search the papers themselves, call `search_literature` with `collection_slug` set to this slug. **Parameters:** - slug: The Collection slug (required). - include_notes: If true, embed the note index described above (default: false). **Returns:** The Collection with id, slug, name, description, isPublic, accessType, DOI counts, noteCount, and `notes` when `include_notes` is set.
get_collection
Fetch full details for a single FDA 510(k) clearance by K number. Use this after `search_device510k` when you need the complete record for a specific clearance, including the full `summaryText` (the complete 510(k) summary statement, often very long), full `applicant` details (address, contact, country), `registration` info (FEI and registration numbers), and the complete `decision` object (code, description, committee, review flags). The search tool returns a brief highlighted snippet of the summary text; call this tool for a specific K number when you need the full text or detailed applicant/registration information. **Parameters:** - id: K number identifier (e.g. `K210674`). **Returns:** A compact detail record with full summary text, complete applicant information, registration details, and decision metadata.
get_device510k
Fetch full details for a single FDA drug record by ID. Use this after `search_drugs` when you need the complete record for a specific drug, including every approved product (product number, applicant, approval date, dosage form, route, active ingredients, TE code) and the full Structured Product Label text sections. The search tool returns a slim view; this tool adds detail-only fields. **Parameters:** - id: Drug record ID (the UUID from search_drugs results, e.g. `4dd865ec-8889-49ac-8c8f-4438875937ac`). **Returns:** A detailed drug record with the application's products and the main label sections (indications and usage, dosage and administration, contraindications, boxed warning, warnings and cautions, adverse reactions, drug interactions, use in specific populations, pharmacology, clinical studies, how supplied, overdosage, description).
get_drug
Fetch full details for a single FAERS adverse event report by ID. Use this after `search_faers` when you need the complete record for a specific report, including patient demographics, full drug dosage details, the reporting source, and any duplicate-report references. The search tool returns a slim view; this tool adds detail-only fields. **Parameters:** - id: FAERS safety report ID (e.g. `26185565`). Obtained from search_faers results. **Returns:** A detailed FAERS report with patient demographics (sex, age group, onset age, weight, death date), report dates (receiveDate, receiptDate, transmissionDate), expedited flag, primarySource (reporter qualification and country), reportDuplicates, and enriched drugs (dosage text, start/end dates, NDC, application number, pharmacologic class).
get_faers_report
Fetch full details for a single grant by id. Call this after `search_grants` only when you need something the search result does not already have. Specifically, this returns: - Full `abstract` (search returns only a ~300-char highlighted preview; the full text is typically 1-3 KB). - Source-specific identifiers that search does not include: `awardYear`, `agencyTrackingNumber`, `contract`, `nihProgramCode`, `nihrApplicationId`. All other fields (title, agency, organization, piName, country, dates, awardAmount, tags, externalLink, etc.) are already present in search results — don't call `get_grant` just to get those. Also use this to pull siblings listed in `siblingGrantIds` on a search result: call `get_grant` once per sibling id you need. **Parameters:** - id: Grant identifier returned by `search_grants` (e.g. `5201339`, `nsf.0646294`, `wellcome.214402.Z.18.Z`). **Returns:** A compact detail record with the full abstract and the source-specific identifiers listed above; drops the low-signal `categories` ontology.
get_grant
Fetch full details for a single MAUDE adverse event report by ID. Use this after `search_maude` when you need the complete record for a specific report, including the full narrative text (MDR text with text type codes), reporter information, device availability, patient treatment, and tags. The search tool returns truncated text snippets; this tool returns the full narratives which can be much longer. **Parameters:** - id: MAUDE report ID (e.g. `17343805`). Obtained from search_maude results. **Returns:** A detailed MAUDE report with full narrative text entries (with text type codes like "Description of Event or Problem"), reporter occupation, health professional flag, device medical specialty and availability, patient treatment, product problem flag, and tags.
get_maude_report
Fetch the full text of a single MHRA alert or publication by document ID. Use this after `search_mhra` when you need the complete text of an alert, including the full article body (contentHtml), not just search snippets. Returns the full extracted text organized by page. **Parameters:** - id: Document identifier from `search_mhra` results. **Returns:** The full-text content of the MHRA alert, including headline, description, article body, tags, and per-page content.
get_mhra_alert
Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.
get_more_tools
Read the notes written on one Collection, newest first. Notes are free-text Markdown the user and their teammates keep on a Collection — reading lists, synthesis, decisions, open questions. Read them before answering a question about a Collection: they carry the user's own context, which the papers themselves do not. `get_collection` reports `noteCount` so you know whether there is anything here. Unlike the papers in a Collection, notes are team-internal: only members (the owner, users it is shared with, and the organization it is shared with) can read them. A public Collection does not expose its notes. **Paging.** Returns `limit` notes with their full bodies, starting at `offset`, plus `total` — the number of notes the Collection holds, or the number matching `text_search` when that is given. A body can be 20,000 characters, so the default page is 20 and the maximum is 50; page with `offset` rather than asking for everything at once. **Searching.** `text_search` filters to notes whose title or body contains the text. It matches a note as it renders, so Markdown and HTML markup does not block a match: "mindfulness" finds a body written `mind**fulness**`, and "H2O" finds `H<sub>2</sub>O`. Prefer it over paging through everything when looking for something specific. **Parameters:** - slug: The Collection slug (required) — from `create_collection` or `search_collections`. - limit: Notes per page, 1-50 (default: 20). - offset: Notes to skip, for paging (default: 0). - text_search: Filter to notes containing this text (default: no filter). **Returns:** `{notes: [{id, title, body, bodyFormat, createdBy, lastEditedBy, createdAt, lastUpdated}], total, limit, offset}`. Use a note's `id` with `update_collection_note` or `delete_collection_note`.
list_collection_notes
Read a paper's body text by DOI, one page of characters at a time. Use this when you need the ACTUAL text of a paper — not term-matched snippets. It returns the body sliced by character offset so you can page through the whole document. **How it differs from other tools:** - `search_literature` returns up to 5 term-matched ~500-char excerpts — good for finding passages, not reading straight through. - `get_full_text` returns a proxy stitched only from sentences that cite other works — lossy, citation-only. - `read_fulltext` (this tool) returns verbatim body text, linearly, with pagination. **What you get — the `source` field:** - `"fulltext"` — verbatim full text, for open-access papers with a permissive license (or papers your org is entitled to) that have indexed full text. - `"abstract"` — the paper's abstract, returned as a fall-back when verbatim full text is access-restricted or not indexed. Abstracts are public, so most papers return at least this. - `null` — no readable text at all; use search_literature's `access` field for a link. `contentDenied` is true whenever full text was NOT served — i.e. any time `source` is not `"fulltext"` (access-restricted, not indexed, or nothing). So `source: "abstract"` still has `contentDenied: true`. Always check `source`: if it is not `"fulltext"` you are NOT reading the full paper. The `message` field explains why. **Paging:** each call returns up to 8000 characters. Read the first page with `offset: 0`, then set `offset` to the previous `offset + returnedChars` while `hasMore` is true. `totalChars` is the length of whatever `source` you got. Character offsets are only stable within a session — do not persist them across days (re-indexing shifts positions).
read_fulltext
Remove DOIs from a Collection. Works on both DOI-list and saved-search Collections. Requires EDITOR or ADMIN access. For a DOI-list Collection the DOIs are dropped from the list. For a saved-search Collection they are excluded (added to the exclude list) so they no longer appear even if the search would return them. DOIs not present are ignored. This removes papers from the Collection; it does not delete the Collection itself (use `delete_collection` for that). **Parameters:** - slug: The Collection slug (required). - dois: List of DOI strings to remove (required, non-empty). **Returns:** The updated Collection with id, slug, name, and DOI counts.
remove_dois_from_collection
Record your answer's full source decision set — what you cited and what you excluded, each with a reason and its provenance — as a verifiable, auditable citation record. Call this ONCE at the very end of a response that drew on sources, with your full decision set: - every source you CITED (credited in the answer), and - every source you retrieved/considered but EXCLUDED, each with a short reason. Report only sources you actually used — never invent references. Fire-and-forget: it records the decisions and does not change your answer. Use it for: - Fact-checking / reducing hallucinations: works with search_literature (every cited source must trace to a real retrieved record), citation_graph (screen the literature by citation topology, then log which edges you kept vs. dropped and why), and bibliography (references built from stored metadata, not memory). Recording each decision — then reviewing it with citation_report before you finalize — surfaces fabricated, misattributed, or unsupported citations. - Provenance: `source` records WHERE each source came from — scite_mcp (retrieved via scite), web_search, user_supplied, or other — so a reader can tell verified retrievals from unverified ones. - Systematic review / PRISMA screening: the `excluded` items with `reason_code` and `stage` are the screened-out log with reasons at each stage (title/abstract vs full text) that PRISMA requires; the `cited` items are the included studies. - Regulatory / evidence submissions: a reproducible, per-source trail of what was included, what was excluded, and why — auditable straight from the recorded decisions. Each citations item: - source_ref: the DOI (preferred) or, for non-scite sources, a URL/reference string. - decision: "cited" (included/credited) or "excluded" (screened out). - source: provenance — "scite_mcp", "web_search", "user_supplied", or "other". - source_detail: name the source when source is "other" (e.g. "arxiv", "google scholar"). - reason_code: short reason — for excluded: off_topic, retracted, contradicted, duplicate, low_quality, superseded, out_of_scope; for cited: e.g. supports, relevant. - reason: optional free-text note explaining the decision. - stage: optional PRISMA screening stage — "title_abstract" or "full_text". Returns JSON: recorded_cited, recorded_excluded, skipped (malformed items dropped), mcp_session_id, and the accepted decisions grouped as cited[] and excluded[] (each item with source_ref, source, source_detail, reason_code, reason, stage) so a client can render a used/rejected citation panel.
report_citations
Search the full text of FDA 510(k) summary PDF documents. This dataset contains OCR'd full-text content from FDA 510(k) premarket notification summary PDFs. Unlike `search_device510k` which returns structured clearance metadata (device class, applicant, decision codes), this tool searches the actual narrative text of 510(k) submissions and returns matching page-level snippets. Use this tool when the question involves the *content* of a 510(k) submission rather than its metadata. Common triggers: test results, performance data, biocompatibility, substantial equivalence comparisons, indications for use, predicate device comparisons, sterilization methods, software descriptions, bench testing, or clinical study summaries. When you already have a K number from `search_device510k`, use `get_510k_summary` to read the full document instead of searching again. **Parameters:** - q: Search query (technical terms, device descriptions, test methods, etc.) - f: Space-delimited filters in `field:"value"` format - p: Page number (default: 1) **Returns:** Documents with id, filename, tags, and page-level content snippets showing where the query matched within each 510(k) summary PDF.
search_510k_summaries
Search clinical trials from the scite clinical trials database (ClinicalTrials.gov). Use this tool to find clinical trials related to diseases, interventions, sponsors, or research topics. Returns trials with titles, brief descriptions, sponsors, facilities, conditions, interventions, phase, and dates. Highlighted `<strong>...</strong>` snippets indicate which fields matched the query. **Parameters:** - q: Search query string (keywords, condition, intervention, sponsor, NCT id, etc.) - f: Space-delimited filters in `field:"value"` format (e.g. `conditions:"Cancer" trialState.phase:"Phase III"`) - p: Page number (default: 1) - s: Sort field (default: _relevance). Options: - _relevance: relevance score (sortDir ignored) - dates.startDate: trial start date - dates.completedDate: trial completed date - dates.lastUpdatedDate: last update date - sortDir: Sort direction, asc or desc (default: desc). Ignored when s is _relevance. **Returns:** Clinical trials with nctId, title, briefDescription, phase, sponsors, facilities, conditions, interventions, tags, startDate, completedDate, and publicationCount.
search_clinical_trials
List the Collections the signed-in user can access, with an optional name filter. Returns Collections the user owns, is shared on, or that are shared with their organization. Pass `q` to filter by a case-insensitive substring of the Collection name. This is a filter over the caller's own Collections, not a full-text search of all Collections. **Parameters:** - q: Optional case-insensitive name substring to filter by. **Returns:** `{collections: [...], total: N}` where each Collection has id, slug, name, accessType, and DOI counts.
search_collections
Search FDA 510(k) premarket notification clearances from the scite device database. Use this tool to find medical device clearances by device name, product code, applicant, clearance type, or K number. Returns clearances with device details, decision info, applicant information, and regulatory classifications. 510(k) is the FDA's premarket notification process -- manufacturers must demonstrate that their device is substantially equivalent to a legally marketed device before it can be sold. **Parameters:** - q: Search query string (device name, product code, applicant, K number, etc.) - f: Space-delimited filters in `field:"value"` format (e.g. `device.deviceClass:"2" decision.decisionCode:"SESE"`) - p: Page number (default: 1) - s: Sort field (default: _relevance). Options: - _relevance: relevance score (sortDir ignored) - device.device_class: device risk classification - device.date_received: date FDA received the submission - decision.decision_date: date of FDA decision - sortDir: Sort direction, asc or desc (default: desc). Ignored when s is _relevance. **Returns:** Device 510(k) clearances with kNumber, title, summaryText, device info (name, class, productCode, clearanceType, regulationNumber), decision info (code, description, date, committee), applicant details, and tags. **Note:** This tool returns structured clearance metadata only. For the actual narrative content of 510(k) summary documents (test results, substantial equivalence reasoning, indications for use, performance data), use `search_510k_summaries` instead.
search_device510k
Search FDA drug records: Structured Product Labels, the Orange Book, and Drugs@FDA. Each result bundles an FDA drug application (approved products, applicant, approval dates, marketing status) with its Structured Product Label (indications, warnings, pharmacology, etc.). Use this to find approved drugs by name, active substance, manufacturer, pharmacologic class, or indication. **Parameters:** - q: Search query (brand name, generic name, active substance, indication, etc.) - f: Space-delimited filters in `field:"value"` format. Facet fields: labels.brand_name, labels.generic_name, labels.substance_name, labels.manufacturer_name, labels.product_type, labels.route, labels.pharm_class_epc, labels.pharm_class_moa, labels.pharm_class_cs, labels.rxcui, labels.unii, labels.product_ndc, labels.package_ndc, application.application_number, application.sponsor_name, application.products.marketing_status, application.products.dosage_form, application.products.route, application.products.product_type, application.products.te_code, tags, categories. Date range: use labels.effective_time or application.products.approval_date with gte/lt suffix (e.g. labels.effective_timegte:"2024-01-01"). - p: Page number (default: 1) **Returns:** Drug records, each bundling an FDA application (approved products, applicant, approval dates, marketing status) with its Structured Product Label (indications, warnings, pharmacology). Sorted by relevance only.
search_drugs
Search FDA FAERS (FDA Adverse Event Reporting System) drug adverse event reports. Use this tool to find adverse event and medication error reports submitted to the FDA for drugs and therapeutic biologics. Each report links one or more suspect/concomitant drugs to the patient reactions that were observed. Returns reports with the drugs involved, patient reactions (MedDRA preferred terms), seriousness, and report metadata. **Parameters:** - q: Search query string (drug brand or generic name, active substance, reaction term, etc.) - f: Space-delimited filters in `field:"value"` format - Facet filters: - `drug.medicinalproduct` -- reported drug name (e.g. "IBUPROFEN") - `drug.brand_name` / `drug.generic_name` / `drug.substance_name` -- product names - `drug.manufacturer_name` -- manufacturer/labeler - `drug.drugindication` -- reported reason for use (e.g. "Pain") - `drug.pharm_class_epc` / `drug.pharm_class_moa` -- pharmacologic class - `reaction.reactionmeddrapt` -- patient reaction MedDRA term (e.g. "Nausea") - `reaction.reactionoutcome` -- reaction outcome (e.g. "Recovered/Resolved", "Fatal") - `event.reporttype` -- report type (e.g. "Spontaneous") - `event.seriousness_type` -- seriousness category (e.g. "Death", "Hospitalization") - `event.occurcountry` -- country where the event occurred - `event.patientsex` -- patient sex - `primarysource.qualification` -- reporter type (e.g. "Physician", "Consumer") - Date range filters on `event.receivedate`, `event.receiptdate`, `drug.drugstartdate`, `drug.drugenddate`: - Suffix notation: append `gte` (>=) or `lt` (<) to the field name. Example for H1 2024: `event.receivedategte:"2024-01-01" event.receivedatelt:"2024-07-01"` - p: Page number (default: 1) **Returns:** FAERS reports with safetyReportId, title, reportType, serious flag, seriousnessType, receiveDate, occurCountry, reactions (reaction term + outcome), and drugs (medicinalProduct, brandName, genericName, substanceName, indication, actionDrug, route, pharmacologic class). FAERS sorts by relevance only.
search_faers
Search research grants from the scite grants database (NIH RePORTER, NSF, SBIR/STTR, Wellcome, EU, and more). Use this tool to find grants by research topic, PI, organization, agency, or funding keywords. Returns grants with title, a short abstract preview, agency, organization, PI, country, dates, awardAmount, tags, and externalLink. Highlighted `<strong>...</strong>` fragments indicate which fields matched the query. **Grouping.** Resolute groups related grants under one shared slug (e.g. NIH subprojects of one center grant, or renewals of the same award). Each search result returns **only one representative grant** per group. `grantsInGroup` tells you how many total grants exist in the group; `siblingGrantIds` (when present) lists the other grant ids in the group. To pull the full record for a specific sibling, call `get_grant` with its id — do not re-search. **Abstract in search is a ~300-char highlighted preview, not the full text.** Call `get_grant` when you need the full abstract (often 1-3 KB) or source-specific identifiers (`awardYear`, `agencyTrackingNumber`, `contract`, `nihProgramCode`, `nihrApplicationId`). If the search result already contains the fields you need, do not call `get_grant`. **Parameters:** - q: Search query string (keywords, PI name, organization, agency, etc.) - f: Space-delimited filters in `field:"value"` format (e.g. `agency:"NIH" country:"United States"`) - p: Page number (default: 1) - s: Sort field (default: _relevance). Options: - _relevance: relevance score (sortDir ignored) - awardStartDate: grant start date - awardCloseDate: grant close date - awardNoticeDate: grant notice date - awardAmount: total award amount - employeeCount: PI employee count - sortDir: Sort direction, asc or desc (default: desc). Ignored when s is _relevance. **Returns:** Grants with id, title, abstract snippet, agency, organization, piName, country, award dates, awardAmount, tags, groupSlug, and grantsInGroup.
search_grants
Search scientific literature and read full-text content from peer-reviewed papers. Use `dois` (preferred) or `titles` with targeted `term` queries to extract full-text passages from specific papers. Each call returns up to 5 relevant excerpts (~500 chars each) — vary search terms across calls to read through a paper section by section. **IMPORTANT — keep `limit` small.** Use `limit: 10-50` with `offset` for pagination. Large limits with full citations and excerpts produce very large payloads that consume significant LLM context. **Calling with no parameters browses the corpus** (210M+ papers, relevance-sorted). This is allowed for broad exploration but rarely what you want — pass `term`, `dois`, `titles`, or other filters for targeted results. **What This Tool Returns:** - Paper metadata: title, authors (first 3), abstract, DOI, journal, year, volume, issue, page - `fulltextExcerpts`: up to 5 passages (~500 chars) from the paper matching your query (OA only) - `access`: resolved access link with source, type (open/institutional/purchase), content type, and pricing - `citations`: Smart Citation statements — actual quoted text from citing papers, classified as supporting/contrasting/mentioning/unclassified (unclassified = statement present but classifier hasn't assigned a type) - `tally`: citation metrics (total, supporting, contrasting, mentioning, citing publications) - `editorialNotices`: editorial notices (retraction, correction, concern, erratum), each with status, noticeDoi, date - `isOa`, `oaStatus`, `license`: open access information **Fetching Paper Metadata (no search term needed):** Pass `dois` or `titles` WITHOUT a `term` to retrieve metadata for specific papers. Example: `dois: ["10.1038/s41586-020-2012-7"]` **Full-Text Excerpts:** For OA papers, `fulltextExcerpts` contains passages matching your query. If empty, the full text is not indexed or terms didn't match — use the `access` field for the best link to the PDF or full text. **Smart Citations ARE Full-Text Evidence:** - `snippet`: exact sentence/paragraph from the citing paper's full text - `type`: classification (supporting, contrasting, mentioning, unclassified) - `section`: paper section (Introduction, Methods, Results, Discussion) - `sourceDoi`: paper containing this snippet; `targetDoi`: paper being cited **Search Capabilities:** - Boolean operators: AND, OR, NOT - Phrase search: "exact phrase" - Proximity: "term1 term2"~5 - Field filters: title, abstract, author, journal, year, affiliation - Citation filters: supporting_from/to, contrasting_from/to, mentioning_from/to - Editorial filters: has_retraction, has_concern, has_correction, has_erratum **Parameters:** - `term`: cross-field search query (optional when `dois`/`titles` provided) - `dois`: array of DOIs to filter to specific papers - `titles`: array of titles to filter (use when DOIs unavailable) - `limit`: max results (default: 10, max: 1000) - `offset`: pagination offset - Plus 20+ filter parameters (see schema) **Response Format:** ```json { "hits": [{ "doi": "10.1234/example", "title": "Paper Title", "authors": [{"authorName": "Jane Smith"}], "abstract": "Full abstract text...", "year": 2023, "journal": "Nature", "tally": {"supporting": 32, "contrasting": 8, "mentioning": 5}, "fulltextExcerpts": ["Relevant passage..."], "access": {"url": "https://...", "accessType": "open", "contentType": "pdf"}, "citations": [{"snippet": "These findings...", "type": "supporting", "section": "Results"}], "editorialNotices": [{"status": "retracted", "noticeDoi": "10.1234/notice", "date": "2021"}] }] } ```
search_literature
Search FDA MAUDE (Manufacturer and User Facility Device Experience) adverse event reports. Use this tool to find medical device adverse event reports, including device malfunctions, patient injuries, and deaths reported to the FDA. Returns reports with device information, event descriptions, patient problems, and narrative text snippets. **Parameters:** - q: Search query string (device name, manufacturer, event description, product code, etc.) - f: Space-delimited filters in `field:"value"` format - Facet filters: - `event_type` -- Injury, Death, Malfunction, Other, No answer provided - `device.device_class` -- device risk class: 1, 2, or 3 - `device.manufacturer_d_name` -- manufacturer (e.g. "Medtronic") - `device.device_report_product_code` -- FDA product code (e.g. "DTB") - `device.regulation_number` -- regulation number (e.g. "870.3680") - `report_source_code` -- Voluntary report, Manufacturer report, etc. - `product_problems` -- reported device problems (e.g. "High Capture Threshold") - `patient.problems` -- patient problems (e.g. "Death", "Atrial Fibrillation") - Date range filters on `date_received` or `date_report`: - Suffix notation: append `gte` (>=) or `lt` (<) to the field name. Example for H1 2024: `date_receivedgte:"2024-01-01" date_receivedlt:"2024-07-01"` - Comma notation: `date_received:"2024-01-01,2024-07-01"` (gte,lt) - p: Page number (default: 1) - s: Sort field (default: _relevance). Options: - _relevance: relevance score (sortDir ignored) - date_received: date FDA received the report - date_report: date of the original report - sortDir: Sort direction, asc or desc (default: desc). Ignored when s is _relevance. **Returns:** MAUDE reports with id, title, reportNumber, eventType, adverseEventFlag, productProblems, device info (brandName, genericName, manufacturer, deviceClass, productCode, modelNumber), patientProblems, dates, and narrative text snippets.
search_maude
Search MHRA (Medicines and Healthcare products Regulatory Agency) safety alerts and publications. This dataset contains full-text content from MHRA drug safety alerts, medical device alerts, field safety notices, and regulatory publications. Search covers headlines, descriptions, and page-level document content. Use this tool when the question involves UK drug safety communications, MHRA medical device alerts, field safety notices, drug recalls, or MHRA regulatory guidance. **Parameters:** - q: Search query (drug names, device types, safety issues, alert topics, etc.) - f: Space-delimited filters in `field:"value"` format. - Facet filters: `ontology.tags`, `ontology.categories`, `domain` - Date range filters on `attachments.file.createdAt` or `attachments.file.modifiedAt`: - Suffix notation: append `gte` (>=) or `lt` (<) to the field name. Example for Q4 2025: `attachments.file.createdAtgte:"2025-10-01" attachments.file.createdAtlt:"2026-01-01"` - Comma notation: `attachments.file.createdAt:"2025-10-01,2026-01-01"` (gte,lt) - Accepted date formats: YYYY-MM-DD, YYYY-MM-DDTHH:MM:SS, YYYY-MM-DDTHH:MM:SS+ZZZZ, or epoch milliseconds. - p: Page number (default: 1) **Example queries:** - Immunosuppressant alerts in Q4 2025: q="immunosuppressant", f='attachments.file.createdAtgte:"2025-10-01" attachments.file.createdAtlt:"2026-01-01"' - All drug safety updates since March 2025: q="drug safety update", f='attachments.file.createdAtgte:"2025-03-01"' - Medical device alerts from gov.uk: q="medical device alert", f='domain:"gov.uk"' **Returns:** Alerts with id, headline, description, tags, categories, date, and page-level content snippets showing where the query matched.
search_mhra
Search patent families from the scite patents database. Use this tool to find patents related to scientific research topics. Returns patent families with titles, abstracts, inventors, assignees, filing status, and citation counts. **Parameters:** - q: Search query string (keywords, inventor name, assignee, etc.) - f: Space-delimited filters in key:value format (e.g. "assignee:Pfizer filing_status:granted") - p: Page number (default: 1) - s: Sort field (default: _relevance). Options: - _relevance: relevance score (sortDir ignored) - forwardCitationCount: number of forward citations - familySize: number of patents in the family - patents.publications.pubRef.date: publication date - patents.appRef.filingDate: application filing date - sortDir: Sort direction, asc or desc (default: desc). Ignored when s is _relevance. **Returns:** Patent families with metadata including title, abstract, inventors, assignees, classifications, and publication references.
search_patents
Update a DOI-list Collection the signed-in user can edit. Partial update: only the fields you supply change; omitted fields keep their current values. Omitting `dois` leaves the DOI list untouched; supplying `dois` replaces it (unknown DOIs are dropped and surfaced via `unmatchedDoiCount`). Requires EDITOR or ADMIN access. Only DOI-list Collections can be updated here — saved-search Collections are managed in the scite web app. **Parameters:** - slug: The Collection slug (required). - name: New name (optional). - description: New description (optional). - dois: Replacement DOI list (optional; omit to leave DOIs unchanged). - is_public: New public flag (optional). **Returns:** The updated Collection with id, slug, name, accessType, and DOI counts.
update_collection
Edit a note on a Collection. Only the note's author may edit it. Partial update: supply just the fields that change and the rest keep their current values. Supplying `body` replaces the whole body, so to append to an existing note read it first with `list_collection_notes` and send the combined text. Editing a teammate's note is not allowed on any scite surface — it fails rather than silently rewriting their work. Use `create_collection_note` to add your own note instead. **Parameters:** - slug: The Collection slug (required). - note_id: The note's numeric id (required) — from `list_collection_notes`. - title: New title (optional). - body: Replacement body (optional). - body_format: "MARKDOWN" or "PLAIN" (optional). At least one of title, body, or body_format must be supplied. **Returns:** The updated note with id, title, body, bodyFormat, createdBy, lastEditedBy, and timestamps.
update_collection_note
How do I improve a ChatGPT Plugin's discoverability?
The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.
What are Scite alternatives on ChatGPT?
As of 2026-09-28, Scite competes with Aarth, Agentic Search by Liner, AllNutrition, alphaXiv, Article Galaxy, fastwrite, NYCU Library, Pendar, Precise Special Functions, SciSpace, Sider Scholar, SuperQDA, Tailo Lens, Undermind, Wiley Scholar Gateway, 史书馆 in ChatGPT Academic Literature Search & Research Assistants, ranked by public Discoverability Score.
Where is this profile measured?
This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.