Maven Bio

Read the content of one or more documents, event source bundles, trial records, or clinical studies. Supports three identifier formats -- use whichever you have: - Maven IDs: "doc_...", "evt_...", or "trial_..." from search_documents, get_recent_events, research_entity, or search_entities results. - NCT IDs: ClinicalTrials.gov identifiers like "NCT04380636". - URLs: any life sciences URL -- a PubMed abstract, an FDA approval letter, a press release. Maven reads and structures the content automatically. Pass a single ID or a list of up to 10 IDs. Each document costs 1 credit. Use max_doc_tokens to control how much content is returned per document. Defaults to 8000. Applies to all formats except "summary". Four formats: "summary" (short), "sections" (structured fields), "text" (full markdown), "citations" (citable claims, requires query param). Start with "summary" or "sections". Escalate to "text" or "citations" only when needed.

read_document

Pull genetic-validation and target/disease evidence from public biomedical databases (Open Targets, gnomAD gene constraint, GWAS Catalog). Use to back a deal thesis or competitive landscape when the signal lives in databases, not literature: target tractability/druggability, gene constraint (LOEUF), gene-disease association strength, and the targets most associated with a disease. Select `aspects` to control what is returned (omit for sensible defaults per entity_type). Valid aspects depend on entity_type: - target: associations, tractability, constraint, known_drugs, repurposing (default: associations, tractability) - disease: associations, gwas, repurposing (default: associations) `known_drugs` is a TARGET-side aspect: it lists the drugs in development AGAINST the target you named. It is not a drug lookup. Takes a target or a disease, not a drug. For a drug's mechanism, targets, or indications use research_entity; for the drugs hitting a target use entity_type='target' with aspects=['known_drugs']. For a specific target-disease pair, pass entity_type='target' with the disease in `context`. Repurposing hypotheses are opt-in via aspects=['repurposing'] and are always speculative. Each result carries an evidence_rating (Direct/Indirect/Minimal). Database-curated associations are Direct; inferred scores are Indirect; a fuzzy disease-ontology match is downgraded one level. Not for clinical trial data (use research_landscape) or financials (use get_financials).

research_bio_evidence

Get comprehensive data on a specific drug, company, trial, mechanism, target, or development program. Pass only the raw entity name in `name` -- Maven resolves variants internally (e.g. "pembro", "Keytruda", "pembrolizumab" all work). Do NOT pass IDs here; pass names. The one exception is `program`, which has no name: pass a `prog_` id in `name`, or the drug name with the indication in `context`. See match_entity. If you have extra identifying context such as a sponsor, company, or disambiguating note, pass that in `context` rather than packing it into `name` (e.g. `name="Tenecteplase"`, `context="Genentech"`). Use `aspects` to request specific data sections. Omit for the default aspects. Valid aspects depend on entity_type: - drug: trials, events, documents, indication_phases - company: trials, events, documents, financials [+ opt-in: financings, investments, deals] - trial: events, documents - mechanism: trials, events, documents, indication_phases - target: trials, events, documents, indication_phases - program: development_success (opt-in; the program's only aspect) Opt-in program aspect: `development_success` returns a published historical rate at which programs entering this program's phase in its disease area went on to approval. It is a cohort base rate, not a forecast for this asset and not a probability conditional on the current phase already being underway. Where the indication spans several of the source's disease areas the rate combines those cells and says which. When the program cannot be matched to a cell the block states why instead of returning a number. Opt-in company aspects: `financings` returns funding rounds where the company is the recipient; `investments` returns rounds where the company is an investor (useful for VCs, corporate venture arms, and investor companies); `deals` returns licensing, M&A, and collaboration deals where the company is any party, each row carrying a per-party `role`. All require explicit opt-in via the aspects argument. For cross-company deal queries (every deal matching some criteria, not anchored to this company), use get_deals instead (filterable by indication (hierarchy-expanded, includes broad areas like oncology), company, role, deal type, value, and date). Each financing round and deal carries a `value_status` ("confirmed" or "unconfirmed"); when "unconfirmed", the USD value is null and the amount is not in Maven's data, not necessarily missing in the world. Even when you do not opt in, the default company response includes `related_previews.financings`, `related_previews.investments`, and `related_previews.deals` teasers (count plus a few sample rows) plus a follow-up hint, so you can see at a glance whether funding or deal activity exists before requesting the full per-row detail. When you request specific aspects, only those sections are returned; a hint lists the other available aspects so you can fetch them in a follow-up call if needed. Aspects that do not apply to the given entity_type (for example `financials` on a drug, or `indication_phases` on a company) are ignored rather than causing an error, and a hint reports which were dropped. Establishes the baseline fact pattern for one entity. search_documents and read_document provide source-backed evidence for the claims in this profile. If you only need canonical single-entity lookup without the full profile, use match_entity. If entity resolution fails, the error will suggest alternatives. For indication-level competitive landscapes, use research_landscape instead.

research_entity

Get a competitive landscape view for a therapeutic indication. Returns drug rows, each summarising that drug's programs in the indication, grouped by phase, company, or mechanism. For the finer program unit (one drug in one indication and setting), use search_entities(entity_type="program"). Pass the raw indication name in `indication` -- "NSCLC", "non-small cell lung cancer", "triple-negative breast cancer" all work. If you have additional disambiguating context, pass it in `context` rather than packing it into `indication`. This tool takes ONE indication, itself plus its descendants -- so for a broad commercial area ('immunology', 'heme onc', 'cardiometabolic'), pass a broad umbrella indication (e.g. 'Immune System Diseases', 'Neoplasms') and it expands to the descendants, then drill into specific indications here. Programs are ranked by indication-specific phase so approved and late-stage assets surface first. The response is exact and paginated over the full matching program set, not a sampled baseline. By default, returns a compact matched-context response: each program foregrounds the indication, phase, and a capped sample of trials that explain why it matched the query. Set `supporting_trial_limit` to tune the trial sample size, or set `detail_level='full'` for heavier debugging fields such as all companies and all indication-scoped trials. Use `group_by` to control how the returned page is organized: - "phase" (default): programs grouped by development phase - "company": programs grouped by sponsor - "mechanism": programs grouped by mechanism of action. When a `mechanisms` filter is present, programs are grouped by the mechanism values that matched that filter; other co-mechanisms remain on the program as context. Without a mechanism filter, multi-mechanism programs appear in each mechanism group, so mechanism group counts are assignment counts and may sum above `total_programs`. Filters (defaults surface active, non-biosimilar competitive programs): - `drug_phases`: filter on the drug's highest phase for this indication (e.g. ["Phase III", "Phase II"]). Accepts Roman (Phase III) or Arabic (Phase 3). A drug whose highest phase for this indication is "Marketed" is NOT returned by a ["Phase III"] request, even though it ran Phase III programs to get there. None means all phases. - `development_status`: "active" (default), "active_plus_ndr", or "all". - `exclude_biosimilars`: default true. Set false to include biosimilars. - `mechanisms`: filter to specific MoAs (e.g. ["PD-1"]). - `offset`: absolute result offset for pagination. Use `has_more` + `more_via` to continue; do not increment offset by the requested limit because large pages may be truncated to an effective_limit. Each compact program returns: drug_id, drug_name, company_names, mechanisms, indication, phase, status, supporting_trials, and supporting_trial_count. `phase`/`matched_indication_phase` come from indication-specific program rows when available, falling back to indication-scoped trial phases. `status` is retained for compatibility as the first returned supporting-trial status; use `drug_global_status`, `drug_sub_global_status`, and `supporting_trial_statuses` for explicit provenance. When a precomputed relevance score exists for the program in this indication, it also carries a `relevance` block: {score, confidence, signals:{stage, position, activity, differentiation}, drivers, caveats}. `drivers` names the strongest one or two signals in plain words; `caveats` names data-quality flags (e.g. crowded class, readout date is an estimate). Programs without a score omit the block. This score is surfaced to users as 'Prominence'. It is a within-indication competitive read: how prominent a program is among the other programs competing in this same indication, blending its development stage, clinical position, recent development activity, and mechanistic differentiation, normalized against that indication's field. It is NOT a prediction of clinical success, approval likelihood, or commercial quality, and it is comparable only within one indication, never across indications. When narrating it, lead with `drivers`, temper with `caveats`, and treat a missing block as 'no precomputed score', not 'low prominence'. Establishes a competitive landscape baseline. search_documents, read_document, and get_recent_events provide corroborating evidence and recent updates for the programs it returns. For a deep dive on a specific drug, use research_entity. For an indication's recent news, use get_recent_events.

research_landscape

Search Maven's document corpus -- FDA filings, SEC filings, clinical papers, press releases, conference abstracts, and corporate presentations. Returns documents ABOUT entities. To resolve one known entity name, use match_entity. To discover entities by criteria, use search_entities. Use source_types to narrow by document category. Use entity_name to scope results to documents mentioning a specific drug or company. Keep `entity_name` to the raw entity name and put extra identifying text such as sponsor/company in `context`. This tool is for generic document discovery and evidence lookup across Maven's corpus. When host web fetches are blocked, use this as the primary fallback path. Maven often has indexed copies or near-equivalent documents that you can then read with read_document. `search_mode` controls retrieval strategy: 'auto' picks the best Maven retrieval path for the query; 'semantic' uses Maven's internal semantic document search only; 'keyword_strict' uses internal lexical search requiring resolved entity terms when available. To read a document's full content after finding it, use read_document with the doc_id. Pagination: `count` is the number of results in this response. `pagination.total` counts the retrieved window, which is a lower bound on the matching corpus unless `pagination.total_is_exact` is true, meaning retrieval reached the end of the matches. Page while `pagination.has_more` is true, following `more_via`. A `retrieval_depth_reached` hint means retrieval stopped at its own ceiling, so narrowing the query surfaces more than paging deeper will.

search_documents

Find drugs, programs, companies, trials, indications, mechanisms, targets, financing rounds, or deals matching structured criteria. Accepts natural language queries that Maven translates into structured filters. Good queries: "Phase 3 NSCLC drugs", "biotech companies with market cap over 1B", "KRAS G12C inhibitors in solid tumors", "active trials in TNBC", "Series B oncology rounds since 2024 over $50M", "licensing deals over $500M in oncology since 2024", "Phase 3 NSCLC programs". A program is one drug's development in one indication (optionally one setting/biomarker), so questions scoped to an indication want programs, while questions about a molecule across all its uses want drugs. Programs have no canonical name; find them here, not via match_entity. For canonical lookup of a single known entity, use match_entity. For a comprehensive profile of a single known entity, use research_entity instead. For an indication-level competitive landscape, use research_landscape. For company-anchored financing views (one company's rounds or investments), prefer research_entity(..., aspects=["financings"]) or aspects=["investments"]. For precise financing filters by recipient, investor, type, value, or date, use get_financings. For precise deal filters by indication, party, role, deal type, value, or date, use get_deals. If the query looks like single-entity lookup or criteria search returns no meaningful matches, the response may include machine-readable `hints` to guide the next step. Results include typed IDs that can be passed to research_entity, read_document, get_financials, or get_financings for deeper data. Search results are discovery candidates; search_documents, read_document, and research_entity provide source-backed detail on any candidate. For drug searches, when Prominence scoring is available the results are ordered Prominence-first (the highest-signal programs lead, so the first page is not polluted by less relevant matches), and each scored drug carries a `relevance` block: {score, confidence, signals, drivers, caveats}. Prominence (surfaced to users by that name) is a within-indication competitive read: how prominent a program is among the others competing in the same indication, blending development stage, clinical position, recent development activity, and mechanistic differentiation, normalized against that indication's field. It is NOT a prediction of clinical success, approval likelihood, or commercial quality, and is comparable only within one indication, never across indications. No candidate is dropped for low Prominence; treat a missing block as 'no precomputed score', not 'low prominence'. Note on company discovery: results match the filters but are NOT relevance-ranked; broad company queries return large alphabetical candidate sets (match_score may be null) where well-known names can be absent from page 1. For "top companies" or ranking questions, use aggregate_records instead (e.g. entity_type='drug', query='drugs grouped by company' plus your filters) and treat search_entities output as an unranked candidate pool.

search_entities