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get_fda_approvals

Read-only

Returns FDA approval / clearance events: drug approval actions from Drugs@FDA, medical-device 510(k) clearances, and device PMA (premarket approval) decisions. Full history (drugs to 1939, 510(k) to 1976, PMA to the 1960s). This is the BULLISH twin of get_product_recalls — the catalyst dataset for biotech and medtech tickers. Use this when the user asks about: new drug approvals for a company or ingredient, priority-review approvals, tentative generic (ANDA) approvals, device clearances by company or product code, PMA supplements, or to pair an approval date with insider trades / 8-K filings / fundamentals. Sources (filter via the source enum): drugsfda — Drugs@FDA submission actions. One record per submission decision (ORIG = original approval, SUPPL = supplemental). decision_code: AP (approved) | TA (tentative approval — generic approved but blocked by patent/exclusivity). review_priority: PRIORITY | STANDARD — PRIORITY reviews are the higher-signal events. application_number prefix tells the product class: NDA (new drug), ANDA (generic), BLA (biologic). 510k — Device premarket notifications. decision_code SESE ('substantially equivalent' — cleared) dominates ~98%. approval_type: Traditional | Special | Abbreviated. pma — Device premarket approvals (Class III, highest-risk devices — implants, life-sustaining). Originals AND supplements (supplement_number, supplement_reason). decision_code: APPR (approved) | OK30 (30-day supplement accepted) dominate. openFDA ships no description text for PMA codes; decision_description mirrors the code. review_priority='EXPEDITED' marks devices under expedited review; 'PRIORITY' marks priority-review drugs. Empty = standard / not flagged. Company matching: applicant is a substring filter on the sponsor / applicant name AS FILED (e.g., 'Pfizer', 'Boston Scientific'). FDA records carry no ticker or CIK — subsidiaries file under their own names, so try the operating-company name, not the holding company. source_url points at the accessdata.fda.gov detail page (approval letters, labels, review documents). Pure-publisher posture: no derived 'approval odds' or price-impact signals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoDirect lookup ('drugsfda-{applNo}-{subType}{n}', '510k-{kNumber}', 'pma-{pmaNumber}-{suppl|ORIG}'). Fastest path.
limitNoMaximum events to return. Default 50, max 500.
sinceNoDecision date lower bound (YYYY-MM-DD inclusive).
untilNoDecision date upper bound (YYYY-MM-DD inclusive).
sourceNoFilter to one feed: drugsfda (drug approval actions), 510k (device clearances), pma (Class III device approvals).
categoryNoCoarser filter: drug (= drugsfda) or device (= 510k + pma together).
applicantNoCase-insensitive substring against the sponsor / applicant company name (e.g., 'pfizer', 'medtronic').
sort_orderNoDefault: desc (most recent decisions first).
product_codeNoExact FDA device product code (e.g., 'OLO', 'MNQ'). Devices only.
product_nameNoCase-insensitive substring against product/trade/device name AND generic name / active ingredients (e.g., 'semaglutide', 'stent').
decision_codeNoExact decision code: AP / TA (drugs), SESE etc. (510k), APPR / OK30 etc. (PMA).
review_priorityNoPRIORITY (drugs), STANDARD (drugs), or EXPEDITED (devices). The high-signal filter for catalyst hunting.
application_numberNoExact match on the FDA application number (e.g., 'NDA020123', 'ANDA213414', 'K260369', 'P250013').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnlyHint, destructiveHint, openWorldHint), and the description adds genuinely new behavioral context: full historical depth, the sponsor-name matching caveat (no ticker/CIK, subsidiaries file under own names), and the explicit 'pure-publisher posture: no derived approval odds' disclaimer. It omits return/pagination behavior, but that is minor given the annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and scope are front-loaded, and virtually every sentence carries information. The source-feed block is verbose and heavily indented, pushing length well beyond what most callers need, but the density is justified by the 13-parameter surface and four enums.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 13-parameter, zero-required, no-output-schema tool spanning three FDA feeds, the description covers source semantics, decision codes, matching behavior, historical ranges, and data provenance. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds substantial interpretive meaning beyond the schema: decision_code semantics (AP vs TA tentative approvals blocked by patent), application_number prefixes denoting product class (NDA/ANDA/BLA), and review_priority as the 'high-signal filter for catalyst hunting.' This is far beyond what the schema fields state.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a precise verb+resource ('Returns FDA approval / clearance events') and enumerates the three concrete feeds (Drugs@FDA actions, 510(k) clearances, PMA decisions). It explicitly distinguishes itself from a sibling by naming get_product_recalls as its 'BULLISH twin,' so an agent can route without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit trigger list ('Use this when the user asks about: new drug approvals... priority-review approvals... device clearances by company or product code... PMA supplements') and even names cross-tool pairing targets (insider trades, 8-K filings, fundamentals). This is clear when-to-use guidance with an alternative named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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