SciLeads

Translate a natural-language query into a validated SciLeads filter and run the search. Consumes one AI-search credit. Use for the first search of a topic, or for conceptual refinements; prefer refine_search for structural changes (add a city, exclude an org). `userQuery` is capped at 500 characters - pass a focused search phrase, and summarise a longer ask rather than pasting it verbatim (an over-long query is rejected without consuming a credit). The response also returns two optional query improvements it did NOT apply — `pluralVariants` (grammatical forms of a term) and `relatedTerms` (broader/narrower concepts). Their exact JSON shape is in the output schema; offer them as follow-ups per the guidance below. ## Related terms and plural variants — offer as follow-ups, never up-front friction `ai_search` runs the search on the extracted `filter` term(s) **only**. It also returns two kinds of optional query improvements that were **not** applied. Present the results first, then — when it helps — offer these as follow-ups to broaden or narrow. Never gate results on them, never auto-apply them, and **never comment on their quality** (they exist to improve recall; the user judges relevance, not you). To actually apply one the user picks, add it via `refine_search` (structural) or a follow-up `ai_search` (conceptual). **`pluralVariants` — offer FIRST (cheap recall).** Plural/grammatical forms of a term (e.g. *mutation → mutations*) — the same concept, just pluralised, so the lowest-risk way to catch more results. The shape is nested so you can see each variant's source: the outer key is the extracted term, the inner key is the phrase the variant is a plural *of* — the base term itself, or one of its related terms. A variant off the **base term** is a pure recall win; a variant off a **related term** only matters once that related term is adopted. > "Want to include plural variants (e.g. *mutation → mutations*) to catch more results?" **`relatedTerms` — offer AFTER plurals (broaden/narrow by intent).** Ranked related concepts (broader *or* narrower), best first. On the SciLeads platform roughly the top 5 per term are pre-selected; here they are suggestions only, never applied. Frame them by what the user wants — focus vs widen — you need not use all of them, and you may add your own when they fit the natural-language query. > "I can narrow or broaden this — e.g. add *pancreatic ductal adenocarcinoma* to focus, or > *carcinoma* to widen. Want any of these?"

ai_search

Return identity, role flags (account manager, demo mode), license expiry, and CRM authentication status for the calling user. Use to tailor suggestions — skip CRM workflows when no CRM is authenticated, flag expiring licenses, address the user by name.

get_current_user

Search the curated catalog of SciLeads filterable fields. Returns matching field paths with usage descriptions, data types, and nested-query hints. Use to discover whether/how to filter on a concept (e.g. 'country', 'funding', 'researcher name'). Does not consume an AI-search credit.

find_filter_fields

Generate a tailored cold-outreach email message + subject for a single researcher using SciLeads' proven outreach prompt. Reuses the same generator (and per-user daily quota) as the SciLeads portal UI. Either set anchorOnRecentActivity: true to let the server pick the top 1-2 most-recent work items from the researcher's recent activity (recommended for "write me an email to X" flows after get_researcher_intel), OR supply explicit selected* ids (use when the user named a specific work item). The two modes are mutually exclusive; the combined explicit-ids cap is two. messageType currently only accepts "generic". When productId is supplied, the product name + description come from the caller's account catalog (same set get_researcher_intel returns) — the LLM cannot pass free-form product copy.

generate_outreach

Fetch the detail envelope for a single clinical trial. Use the `nctId` returned by `search_researcher_workitems` for category=clinicaltrials, or any ClinicalTrials.gov NCT identifier. Default `verbosity="summary"` returns headline fields (titles, phase, status, dates, sponsor, conditions, interventions, and totals for every collection). `verbosity="full"` adds the brief summary, detailed description, eligibility criteria, study design, capped facility/outcome/mesh/keyword lists, and related-publication/grant rollups. Does NOT consume an AI-search credit.

get_clinical_trial

Fetch the detail envelope for a single funded research project / grant. Use the `awardId` returned by `search_researcher_workitems` for category=funding, or any SciLeads award ID. Default `verbosity="summary"` returns the project facts (title, funder, dates, amount), the lead PI, and the organisation; `verbosity="full"` additionally returns the long-form description, public-health-relevance text, and keywords. Does NOT consume an AI-search credit.

get_funded_project

Fetch the detail envelope for a single publication. Use the `citationId` returned by `search_researcher_workitems` for category=publications, or any SciLeads citation ID. Default `verbosity="summary"` returns headline fields (title, journal, identifiers, date, counts) and stays under ~4KB; pass `verbosity="full"` for the abstract, authors, mesh headings, keywords, topics, grants, and full-text URLs (each capped). Does NOT consume an AI-search credit.

get_publication

Fetch the detail envelope for a single tradeshow poster or session. Use the `posterId` returned by `search_researcher_workitems` for category=tradeshows, or any SciLeads poster ID. Default `verbosity="summary"` returns the session, the presenting researcher, and the show; `verbosity="full"` additionally returns the poster abstract text and abstract-link URL. Honors the caller's HideResearcherEmail claim. Does NOT consume an AI-search credit.

get_tradeshow_poster

Build a deep link the user can click to open SciLeads in the browser. Provide either (recordType + recordId) for a single record, or (entityType + filterJson) to open a saved-filter search.

open_in_portal

Patch an existing filter JSON without re-running natural language. Use for structural changes (add/remove a city, narrow a list); top-level patch keys REPLACE corresponding filter keys. Does NOT consume an AI-search credit.

refine_search