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458,051 tools. Updated 2026-08-14 20:18

"Wikidata" matching MCP tools:

  • Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.
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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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  • Search the Byway atlas of typed places — waterfalls, hot springs, lighthouses, covered bridges, historic sites, beaches, peaks, rest areas and ghost towns. Locate by US state (which also needs a kind), by a point and radius, or by name; a name given alongside a locator narrows it. Every result links to the byway.world page for that place, and every place traces to its OpenStreetMap or Wikidata record. One limit, stated: ghost-town rows live in a separate Wikidata corpus that the point and name searches cannot read, so they are absent from a kind-less search. Reach them with state + kind, or with get_place. Asking for one any other way is refused, never answered empty.
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  • Look up an airport by IATA code (3 letters, e.g. "LHR"), ICAO code (4 chars, e.g. "EGLL"), or free-text name/city search (e.g. "heathrow"). Covers 85,555 airports worldwide (OurAirports, public domain, cross-checked vs OpenFlights + Wikidata). Provide ONE of iata, icao, or query; the optional type filter narrows results. Behavior: read-only; exact code hits return one record; ambiguous name searches return ranked candidates (exact codes first, then larger airports) with match quality reported via the envelope's confidence (basis match_quality); an unknown code errors with a not-found message. Rate-limited (anonymous use: 25 requests/day per IP): a 429 error body carries retry_after_seconds and a Retry-After header — back off and retry, or call get_subscribe_link for higher limits. Returns: count and results[] — per airport: IATA + ICAO/ident, name, type (large/medium/small/heliport/closed/seaplane), municipality, region, country, latitude/longitude and elevation_ft — under result, plus confidence, _source and citation (the FreightUtils v1 response envelope). Limitations: reference data only — not for navigation; verify operationally critical codes with IATA / ICAO. Related: nearest_airport (find airports FROM a coordinate), airline_lookup (searches CARRIERS / AWB prefixes, not airports), unlocode_lookup (general transport locations, of which airports are one function).
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  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.
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Matching MCP Servers

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    MCP server that wraps the Wikidata API, enabling AI agents to query and interact with Wikidata data using natural language through the Pipeworx gateway.
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    A server implementation for interacting with Wikidata API using the Model Context Protocol, providing tools for searching identifiers, extracting metadata, and executing SPARQL queries.
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    MIT

Matching MCP Connectors

  • Wikidata MCP — wraps Wikidata API (wikidata.org/w/api.php)

  • Wikidata SPARQL MCP — Wikidata Query Service

  • Returns the identity of Origine Paris, the Parisian fine jewellery house of recycled 18ct gold and IGI-certified lab-grown diamonds. Use it for ready-to-use brand facts (trading name, legal identity (SIREN), descriptions, positioning, the by-appointment address at 21 rue de la Paix, contacts, official profiles); for the underlying source markup use get_jsonld_graph, and for the catalogue use search_catalogue, not this. Read-only and side-effect-free: it returns a structured identity object plus a text copy, with the sources, the index timestamp and the canonical URL, taken from the site JSON-LD and Wikidata and served as published; absent values are reported as "unknown", never invented.
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  • Returns a detailed, sourced profile of one Origine Paris founder, the recycled gold and lab-grown diamond jewellery house. Use it for a single founder's biography, career with dates and references, roles, education and citizenship; for the two-person roster use get_founders instead. Provide exactly one of name or qid. Read-only and side-effect-free: it returns a structured profile object plus a text copy, with the sources, the index timestamp and the canonical URL, from Wikidata and the site JSON-LD; an unrecognised person yields an explicit "unknown" result, never a guess.
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  • Artist profile by MBID: type (person/group/…), country, life span, gender, area, aliases, tags/genres, plus the discography (release-groups) and band-membership / collaboration relationships and external links (Wikidata QID, Discogs, official site — surfaced as url-rels chainable to those servers). The 80% artist-detail call. Discography and relationships are capped at one page (25); for a prolific artist's complete release-group list, call musicbrainz_browse_entities with target_type=release-group and the artist link.
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  • The surface registry of a project: the pages about the brand where the customer has the FINAL SAY (website, GitHub, LinkedIn, X, YouTube, Wikidata, directories, app stores...). The split with corroborations is control, never who wrote the page: a page the customer can change is a surface, a page where someone else has the final say is a corroboration (list_corroborations). Each surface carries its type, url, label, languages, notes, its checklist and a status DERIVED from the checklist CELLS that hold it: checklist.required lists exactly those, the canon items of the template plus every check the customer added of their own. checklist.kinds answers a different question, what PERISHES a tick: "canon" items restate the canon, so their verification perishes when the wording moves; "presence" ones, such as site_link, hold. A check of the customer holds the status whatever its kind, so read checklist.required and deduce nothing from checklist.kinds. checklist.custom lists those checks, each with its key, label, scope and restates_canon, and the ones taken out with deleted true, which restore_surface_check brings back; add_surface_check is how a new one is posed. Each cell is verified (dated, stamped with the canon version whose WORDING it restated: it stays fresh until the wording moves, and a revision that touches no wording, such as declaring the canonical language, perishes nothing) or set aside with its reason (the item does not apply on THIS surface). Three statuses, never a fourth: aligned when every required cell not set aside is verified at the current wording; needs_update when some verification is missing or stale; never_aligned when none exists. There is no state for a page the canon does not apply to, because setting aside the LAST canon cell is refused with not_a_surface, because a page that carries none of the canon is not a surface: turn it into a corroboration if someone else has the final say on it, or take it out of the registry. checklist.state keeps the flat view of verified items; checklist.progress counts done, total and dismissed, the set-aside cells out of the denominator but never hidden. Each surface also carries domain_authority, what the AIs grant the DOMAIN the page sits on, read from the Atlas: domain is the registrable domain that was measured, engines its AI Authority on each AI over the last 30 days, source_id its entry on the map (read it with get_source). The unit is the domain, so the figure says that PLACE is read, and list_sources reads the very same one. An engine absent from engines has not cited the domain lately, which is not a zero, and a source_id of null means the domain is not on the map of this account, which carries the sources the surveys of this account surfaced and grows as it measures more. Start here to find a surface id. Set deleted to "only" to read the trash of the registry instead of it.
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  • Lookup, search, or browse originators. Handles people, proverbs, anonymous sources, and institutions. Use name= for exact match, search= for fuzzy, neither for browsing. When to use: User asks about a person/author, wants to find who said something, or needs to browse by category (poets, philosophers, etc). Behaviors: - `name` provided → resolve and return single originator details - `search` provided → fuzzy search, return ranked list (optionally filtered by category tags) - Neither → browse by filters (popular, language, min_quotes, category tags, etc.) Category tags filter by originator type (e.g., ["Poets", "Politicians", "Catholic Bishops"]) - works with all modes. Gender filter accepts natural language (e.g., "female", "women", "queer", "trans") - resolved to Wikidata Q-IDs internally. Response format: - Concise (default): slug, full_name, sort_name, quote_count, descriptions_i18n, web_url - Detailed: + biography (500 char excerpt), confidence_tier, similarity_score Response includes ai_hints with suggested next actions and quality signals for agent workflows. Date filters (`born_on`, `died_on`, `born_year_gte`, `born_year_lte`, `died_year_gte`, `died_year_lte`) combine with every other filter via AND. Negative year bounds represent BCE; year 0 is rejected. Examples: - `originators(name="Einstein")` - exact lookup - `originators(search="Shake")` - fuzzy search for "Shakespeare" - `originators(tags=["Poets"], gender="female")` - browse female poets - `originators(sort="popular", limit=10)` - top 10 by quote count - `originators(born_on="04-20")` - originators born April 20 (any year) - `originators(born_year_gte=-500, born_year_lte=-300)` - originators born between 500 BCE and 300 BCE inclusive
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  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1455 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,529 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
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  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.
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  • "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • Returns the two founders of Origine Paris, the house of recycled 18ct gold and IGI-certified lab-grown diamond jewellery. Use it for a quick roster (names, roles, Wikidata QIDs, short bios); for one founder's full biography and career use get_person_profile instead, not this. Read-only and side-effect-free: it returns a structured list of the founders plus a text copy, with the sources, the index timestamp and the canonical URL, taken from the site JSON-LD and Wikidata and served as published; absent values are reported as "unknown", never invented.
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  • Returns the Origine Paris entity graph: the company and its founders as nodes, with the sourced edges between them (founded, chief executive officer, director, employed by). Use it when you need the relationships between entities; for one entity's own fields use get_brand_identity or get_person_profile instead, not this. Read-only and side-effect-free: it returns structured nodes and edges plus a text copy, every edge carrying its sources, with the index timestamp and the canonical URL, built from Wikidata and corroborated by the site JSON-LD; relationships absent from the sources are not asserted.
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  • Fetch one place’s full record by slug (preferred, e.g. "neuschwanstein-castle") or exact name: coordinates, founding year, worldwide fame rank and tier, short description, photo URL, Wikipedia and Wikidata links, and a deep link that opens it on the map. Unsure of the slug? Call search_places first.
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  • Cross-check ONE factual claim against a verified knowledge graph BEFORE you assert it — the "am I sure?" instant, just before a discrete fact leaves your mouth. Prefer this over stating such a fact from memory. Its whole job is to catch your own hallucination before it reaches the user: every call either CONFIRMS your claim with a citable source, CORRECTS it with the real value + source, or honestly ABSTAINS — it NEVER hands back a guessed value, so a call can only help or stay neutral. Honest abstention (UNVERIFIED over a confident guess) is the point, not a failure. INVOKE only when BOTH gates hold: (1) the claim is a lookup-able, atomic subject–relation–object attribute of a real, named entity a public knowledge graph would hold — a who/what/when/where property such as date of birth, place of birth, author, capital, founder, publication date, director, spouse, population — keyed on a real person, place, creative work, organization, event, or species — and, at its highest-confidence tiers, the graph also carries exact values such as a physical constant (CODATA/NIST) or a machine-checked theorem (Metamath), which it verifies best of all; AND (2) being wrong would matter — you are about to state it, cite it, or act on it. Rule of thumb: if you'd attach a citation to it and an error would be consequential, check it here first. One call per uncertain fact, at the moment of doubt — not a per-token or per-sentence reflex. INPUTS — subject: entity name ("Marie Curie") or Wikidata QID ("Q7186"), required; relation: the property as free text ("place of birth"), optional; value: your claimed value ("Warsaw"), optional — omit to just fetch the verified value(s); hint: a short disambiguator ("physicist", "1997 film") when the name is shared. RETURNS {verdict, summary (one-line, ready to quote), confidence (high|medium|none — a coarse tier-derived signal, NOT a probability), subject_id (QID), verified_values, evidence[{rel,value,tier,source}], sources, note}. Verdicts: - SUPPORTED — a verified fact matches; safe to state and cite. Matching is normalized/substring-tolerant, so "exact" means "never guesses," NOT literal string equality — read evidence[].value (or verified_values, present only when you passed NO value) to confirm exact wording. - REFUTED — fires ONLY when the relation has exactly ONE verified value and it differs; verified_values holds the real value + source, so correct yourself and cite it. - NOT_FOUND — the relation has several verified values and yours isn't among them; high-cardinality relations may be incomplete, so weight this softly — it is not a refutation. - UNVERIFIED — not in the verified set, or the subject didn't resolve: it ABSTAINS rather than guess. Read this as "cannot confirm / unknown," NOT "false" — hedge, omit, or fall back to web_search; do not assert. It may queue the gap to learn for next time. This is the trust anchor: a first-class answer, never an error to retry-spam. - AMBIGUOUS — the name maps to several entities (candidates returned); re-call with a hint or a QID. - FACTS — a subject-only call returns that entity's verified facts. Data is Wikidata-scale plus exact-oracle and machine-proven tiers, each fact carrying a source + tier badge; it is sourced, not hand-audited. Prefer cross_check over web_search AND over memory for stable, checkable encyclopedic attributes; use web_search INSTEAD for anything time-sensitive, recent, priced, prose/explanatory, multi-hop, subjective, or non-encyclopedic — and when this returns UNVERIFIED, fall back to web_search rather than asserting. A batch companion, verify_claims, takes a list of {subject, relation, value, hint} to sanity-check a whole draft in one pass. Backend defaults to http://localhost:8791 (override with SPECTRAL_URL); the hosted remote is currently offline — run against a local instance, or after relight. DON'T INVOKE: never as a per-token/per-sentence reflex; never for opinions, evaluations, advice, or forecasts; never for anything you can DERIVE rather than look up (arithmetic, unit/date math, logic, deduction); never for code, API, or library behavior; never for meta-claims about your own reasoning/plan, the task, the user, or local files; never for entities too fictional, private, or obscure to sit in a public graph; never for low-stakes throwaway asides where an error is harmless; and never for time-sensitive, recent, priced, prose/synthesis, or multi-hop questions (those are web_search's job — this answers "is THIS attribute of THIS entity correct?", not "what's happening" or "tell me about"). Do not read UNVERIFIED as a refutation or retry-spam it; do not treat a lenient SUPPORTED as literal string equality without checking verified_values.
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