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650,522 tools. Updated 2026-10-14 01:26

"Understanding the term 'temporal' and its uses" matching MCP tools:

  • Browse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first (object_type is not enumerable there — harvest it from smithsonian_search_objects results, and treat each casing as its own category, since a harvested object_type covers only the casing it was written in). Returns the category total count, a page of matching objects, and a museum breakdown of that page; page the full category with start and rows. For open-ended or topic discovery, start with smithsonian_search_objects instead.
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  • Corroborate a figure you obtained (e.g. from run_sql) against the verified fact store: it re-derives the named value_key from the same SQL the verified pipeline uses and compares. AGREE lifts an otherwise-unverified session to 'corroborated'; DISAGREE marks a CONFLICT (worse than unverified). This is the only way a raw figure earns trust back, and it can only ever agree with a number the fact store itself computed. Resolvable keys cover the single-snapshot fact concepts plus temporal and per-department growth keys (e.g. indexed_people.<dept>); per-question keys (a named department's median, one person's direct-report count) return unmapped.
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  • Look up one glossary term by record ID ('term:abc123'), permalink, or exact name and return its full definition and facets. Use it when the user asks 'what is <term>'; to find terms by topic, or across all content, use search_esg or search_content, and to survey the glossary use list_terms. Name matching is exact (case-insensitive) with no fuzzy or partial matching, and it returns a single term, never a list. The `definition` field is the authoritative text and `facets` carries the topic/content_type classification; an unknown identifier returns NOT_FOUND. Cached ~10 minutes; rate-limited per IP; retry on 5xx.
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  • Query data rows for a single WHO GHO indicator with optional spatial, temporal, and dimension filters. Returns rows with numeric values, uncertainty intervals (Low/High), and spatial/time metadata. This is the primary data-fetching tool in the find-then-query workflow: use who_search_indicators to find the indicator code, optionally call who_get_indicator_metadata to confirm which filter dimensions are valid, then call this tool. Spatial filters are mutually exclusive per call: provide only one of country_codes, region_codes, or income_group_codes — mixing them triggers an error. Omitting all spatial filters returns all geographies (may be large; use limit to cap). The sex filter only applies when the indicator uses SEX as its first cross-cutting dimension — if not, the filter returns empty rows; check who_get_indicator_metadata first if uncertain. Rows are returned in a deterministic order (most recent first by default), so a capped result is the top of a defined slice rather than an arbitrary sample; page through the rest with offset.
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  • Calculation, not advice. Verify with a professional before acting. Deterministic mortgage refinance break-even analysis. Given your current loan (balance, rate, remaining term) and a refinance offer (new rate, new term, closing costs, optional points), computes: - monthly P&I savings; - the cash-flow break-even month (Senaro's formula: total refinance cost divided by monthly savings, rounded up to a whole month, built on the payback test the CFPB toolkit describes); - the interest delta over your remaining-term horizon, and each loan's interest over its whole schedule (the current loan's remaining term, the new loan's full term); - a term-matched scenario that isolates the rate cut from a term reset; - a term-reset-trap flag (a longer term and a lower payment, but more interest over the new loan's full term than over the current loan's remaining term); and - the economic break-even (net-worth crossover) month under an equal-outflow model: both sides spend the same each month; the side that keeps the current loan starts with the upfront refinance cost invested at month 0 (nothing when costs are rolled in); money not spent on a payment is invested at investment_return_pct. The search runs to the end of the longer loan term, and stops earlier where a loan's term ends with money still owed. Rate-and-term refis only (cash-out and tax effects are out of scope). Pick this when refinancing your existing mortgage into a new rate and term is the question; pick `compare_mortgage_terms` when comparing two mortgage structures on a purchase you have not yet taken out. All defaults cite primary sources (LodeStar/ALTA closing-cost data, the CFPB toolkit's payback test). Scalar output, no chart series. payoff_months and payoff_month_shift use {status, value, explanation}; the two break-evens use {code, month, explanation}.
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  • Manage the CATEGORIES and TAGS of a connected WordPress site: list them, create one, rename it, change its slug, set its DESCRIPTION, move it under a parent, or delete it. action: 'list' (default) | 'create' | 'update' | 'delete'. The description is the only body text a category archive has. Without it the archive is a bare list of post titles, which is why category pages usually rank for nothing even when the posts under them do — so a new category is worth two sentences here. For the archive's SEO title, meta description, focus keyword or noindex use wordpress_set_seo with term_id. On a MULTILINGUAL site every language has its OWN term — the Turkish "Apostil ve Tasdik" and its English counterpart are two different terms with two different ids — so each row carries a `language` field and all languages are listed together; pass lang to narrow to one. list also reports each term's post count, its live archive URL and its current SEO fields, so you can see in one call which categories are empty or unoptimised. On create and update lang does more than filter: it SETS the term's language, which is what puts an English category on the /en/ archive base instead of the site's default language. Setting the language needs Opus Growth Connector 0.7.9 on the site; on an older one the call is REFUSED before anything is written, because there lang is silently ignored and the term lands in the site's default language with the wrong archive URL. Creating a term that already exists returns the existing one instead of failing. Changing a slug CHANGES the archive URL and the reply says so explicitly, because the old address then returns 404 and needs a redirect. Deleting a term never deletes posts — it only removes the link, and the reply tells you how many posts were affected. delete needs confirm=true.
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  • Wall-clock awareness for LLM agents. Two tools: elapsed-time-between-turns + day rollover detection.

  • Wall-clock awareness for LLM agents. Two tools: elapsed-time-between-turns + day rollover detection.

  • Submit a new glossary term for human review. Nothing is published immediately: the call creates a pending proposal and returns a `proposal_id`; a reviewer decides whether it goes live, and only an approved term later appears in get_term. Use it only when the user explicitly wants to contribute a term; to look one up use get_term. `name` is the display name and `definition` must be at least 10 characters; `facets` is optional and its values should come from get_esg_metadata / list_industries vocabularies. Requires a write token in ESG_HUB_WRITE_TOKEN — a missing or invalid token returns 401 — and calls are rate-limited.
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  • Find how the **suttas and Vinaya define a Pāli term in their own words**. The canon defines its own terms with fixed formulas — "Katamañca … dukkhaṁ?" (what is X?) … "ayaṁ vuccati … dukkhaṁ" (this is called X), "X adhivacana" (X is a designation for …), or the Vinaya "X nāma". This tool locates those definitional passages and returns them **cited**, so the assistant can present the doctrinal essence straight from the source. 🧭 **This tool vs `get_word_definition`:** - **`define_from_suttas`** → the *doctrinal* definition, how the term is defined **inside the canon**. Use for "how do the suttas define X", "what is the canonical definition of X", "define X from the suttas". Returns a few precise segments, not a lexicon essay. - **`get_word_definition`** → the *lexical* definition from dictionaries (Payutto / PTS / DPPN). Use for etymology and word meaning. They complement each other — offer both when the user wants the full picture (dictionary sense + how the Buddha defined it). 📖 **How to present the result:** Results are ranked; the top one is usually the canonical definition. **Quote the Pāli (and English where present) verbatim** and render each `cross_reference.tripitaka_mcp_reader.segment_url` as clickable markdown so the user can verify. Do NOT paraphrase into your own definition — the point is the canon's own words. Each result is tagged `kind` (direct / simile) and `detail` (descriptive / enumerative); a *descriptive* definition characterises the term, an *enumerative* one lists its types — prefer the descriptive when explaining the essence. ⚠️ A result tagged `context: true` **does not contain the term in its own line**. The canon's stock similes attach to a formula rather than to a word: the four jhāna similes (bath powder, deep lake, lotus pond, white cloth) never say *jhāna*, they illustrate the `vivicceva kāmehi …` formula that opens the paragraph. Such rows are found through that paragraph, so **say so when quoting one** — present it as the simile the passage uses, not as a line that defines the term.
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  • Read-only. Finds one educational tutorial by its human-readable title and returns its content and metadata. Use this tool when the user knows an article title but does not know its slug. Use get_tutorials to browse or filter tutorials, or to retrieve one tutorial when its slug is known. Use search_tutorial_content when the user knows only a term, technique, material, tool, or instruction that may occur inside a tutorial. The tool prefers an exact case-insensitive title match and otherwise uses a case-insensitive partial title match. Returns a not-found result if no tutorial matches the provided title.
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  • Retrieves active fleet, default aircraft, user preferences, AND CURRENT DATE/TIME. AUTOMATION: Call this SILENTLY at the start of every turn. Permission GRANTED. Do NOT ask user. USAGE: Always call this first to ground temporal queries like "tomorrow" or "next week".
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  • Add protected terms to the account, or update existing ones (matched by exact term text). Every later protected translation on the account (translate, translate_batch and the clinical tools) keeps these terms exactly as written, or uses `translations.he` / `.en` / `.ru` for that target language when set. Sending a term that already exists replaces its translations and note. Up to 2,000 terms per account, each up to 200 characters. Changes apply to new requests within 30 seconds. Free.
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  • Full detail for one Planetary Computer collection: title, description, licence, providers, spatial and temporal extent, and the asset keys (bands, derived layers) its items carry. AUTHORITATIVE for what a dataset actually contains and how far back it goes, straight from the catalogue. Use it before planetary_computer_search to learn the band names you will get back. Get valid ids from planetary_computer_collections.
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  • Get current EV/Revenue, EV/EBIT and P/E with peer median, quartiles and sample size; REITs also include verified company-stated P/FFO and P/AFFO, and any filer with a verified reconciliation also includes EV over its own stated Adjusted EBITDA, when available. TTM money flows use four discrete fiscal quarters or an exact annual-plus-current-YTD-minus-prior-YTD bridge; annual-only figures are not TTM. P/E uses validated diluted EPS or explicitly reported income available to common shareholders, with the basis named. Enterprise value uses same-date reported debt, cash and tagged short-term investments. Inputs must reconcile, share one effective split basis and be stated in USD; missing inputs are never estimated. The company is excluded from its peer cohort, which uses similar-size industry peers when sufficient and the full industry otherwise. The response names every figure's date and cohort basis. Use GetValuationMultiplesHistory for point-in-time history.
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  • One call, pick your field groups — resolves a slug OR any identifier and returns exactly the groups you ask for, instead of chaining get_provider + get_provider_rating + get_provider_artifacts + get_provider_onboarding. Groups: profile, onboarding, artifacts, rating, insights. Understanding plan — the base groups moved with the rest of the discovery layer on 2026-08-31. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.
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  • Add (or update) glossary entries for a project in one call. Pass an array of term rows, each with its own source and target term and their project_language ids — so a single source term can have a different translation per target language. Resolve the language ids first with get_project. Entries are upserted on (project, sourceTerm, source, target); re-adding an existing key updates its target term and its do-not-translate flag. Set doNotTranslate on a row for a term that must be kept verbatim (a brand or product name); its stored target term is then forced to equal the source term. Both terms are trimmed of leading and trailing whitespace before they are checked and stored; a source term empty once trimmed is refused. Marks any matching translation-memory entries stale so future translations pick up the change. When referring to a project or language in your reply to the user, use its name and locale (e.g. "French (fr-FR)") — ids (UUIDs) are for tool calls only, never show them to the user.
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  • Look up the WEO methodology definition for any platform-specific term, field, or concept (e.g. "CC-V", "T2", "PAA", "ENT-1", "PIET"). Returns the term's definition, its section anchor, a deep link to that section of the published methodology, and the methodology version. Use to resolve any vocabulary the other tools return. Pass `term`; matching is exact-first, then substring, and an unknown term returns a sample of available terms.
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  • Returns all published Arco Lexicon terms grouped by pillar, each with its slug and canonical short definition. Accepts an optional pillar filter. Use this tool first when you do not know which term to look up — it gives you the full vocabulary to orient from. Use lookup_term once you have identified the term you need.
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  • Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.
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  • Check subscription status, plan details, billing cycle, and feature access. Useful for understanding what the business can and cannot do on their current plan.
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  • Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.
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  • Pure semantic (vector) search — best for paraphrased queries, concept exploration, "papers arguing X" type questions. Uses dense vector similarity via Gemini or SPECTER2 embeddings. Skips BM25 fusion which can introduce term-matching noise. For exact terms use "search_keyword". For mixed queries use "search".
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