Nih Supplements

Quality – / 100not scored

Not scored: fewer than three checks apply, usually because no remote answered a credential-free probe or the server is a local package.

Tool list unknown · tools last changed 2026-10-20 · checked 2026-10-20 09:31 UTC

Tool changes

• 2026-10-20herb_at_a_glanceremoved

  A popular herb's science-and-safety summary from NIH's National Center for Complementary and Integrative Health (NCCIH) 'Herbs at a Glance' series — what the science says, side effects and cautions, and key references. Covers ~56 widely-used herbs (turmeric, ginkgo, echinacea, garlic, ginseng, milk thistle, St. John's Wort, and more) that NIH's Office of Dietary Supplements does NOT have its own fact sheet for. For a vitamin, mineral, or the handful of botanicals ODS does cover (ashwagandha, black cohosh, valerian), use supplement_factsheet instead.

• 2026-10-20supplement_recent_changesremoved

  New supplement product labels entered into NIH's Dietary Supplement Label Database (DSLD) since a given date — the one NIH supplement upstream that publishes a queryable change signal. Returns each new label's id, brand, product name, and entry date, newest first; deterministic on replay. Scope is stated, not guessed: DSLD does not publish off-market (withdrawal) transitions as dated events, and neither ODS fact sheets nor NCCIH herb pages publish a queryable list of content-update dates — the response says so explicitly instead of pretending to cover them. Each supplement_factsheet response carries its own 'reviewed' date for direct sweeping.

• 2026-10-20supplement_labelremoved

  One product's full DSLD label detail: manufacturer contact, every ingredient with its amount, other (non-active) ingredients, and the precaution/warning statements printed on the bottle. Takes the numeric `id` a supplement_labels result returned.

• 2026-10-20supplement_labelsremoved

  Search real supplement product labels — brand name, ingredients, amounts, claims — from NIH's Dietary Supplement Label Database (DSLD). This is what's printed on a bottle, not the science behind an ingredient (use supplement_factsheet or herb_at_a_glance for that). Search by product name, brand, or ingredient. Returns each label's `id` for use with supplement_label.

• 2026-10-20supplement_factsheetremoved

  A specific vitamin, mineral, or supplement's NIH fact sheet — what it does, whether it works, safety, and interactions with medications. Sourced from NIH's Office of Dietary Supplements (ODS). Two reading levels: 'consumer' (short, plain language) or 'professional' (long, clinical, with a references list). Covers ~45 topics (vitamins, minerals, a few botanicals like ashwagandha) — call supplement_factsheets to see the full list. For popular herbs NOT on that list (turmeric, ginkgo, echinacea, garlic, ginseng, milk thistle, St. John's Wort, and more), use herb_at_a_glance instead.

• 2026-10-20rememberremoved

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

• 2026-10-20generate_llms_txtremoved

  Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

• 2026-10-20pipeworx_trendingremoved

  What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

• 2026-10-20classify_rowremoved

  Cheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, evidence_url, quote, status: found|inferred|not_established} — "found" only when the model's quote is independently verified as a verbatim substring of the text used. Pass text directly (cheapest — no fetch) or entity.domain to fetch the homepage once as a fallback. No text and no domain means every label is not_established with no model call made.

• 2026-10-20research_rowremoved

  Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).

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