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521,045 tools. Updated 2026-09-06 10:24

"Wikidata" matching MCP tools:

  • 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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  • 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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  • DEPRECATED, removed in 1.0.0: call get_maison with section "overview" and lang "fr" or "en" instead. Until then this alias still answers exactly as before. 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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  • 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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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    MCP server that wraps the Wikidata API, enabling AI agents to query and interact with Wikidata data using natural language through the Pipeworx gateway.
    18
    MIT
  • A
    license
    A
    quality
    F
    maintenance
    A server implementation for interacting with Wikidata API using the Model Context Protocol, providing tools for searching identifiers, extracting metadata, and executing SPARQL queries.
    5
    48
    MIT

Matching MCP Connectors

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

  • Wikidata SPARQL MCP — Wikidata Query Service

  • Look up a Wikidata entity by an external identifier such as a DOI, PubMed ID, ORCID iD, or OpenAlex ID. Returns match=<entity> on success, match=null when not found, and match=null with multipleMatches populated when a Wikidata data integrity issue causes more than one entity to claim the same external ID. Common cross-server join use cases: CrossRef DOI → Wikidata paper QID (P356), PubMed PMID → Wikidata paper QID (P698), ORCID → author QID (P496), OpenAlex ID → entity QID (P10283). The property must be one whose Wikidata data type is external-id — item-valued or media properties (e.g. P31 instance-of, P18 image) are rejected rather than returning an empty match. Known value normalization is applied automatically: surrounding whitespace is trimmed, identifier-resolver URL prefixes are stripped (https://doi.org/, https://pubmed.ncbi.nlm.nih.gov/, https://orcid.org/), DOIs are uppercased, PMID prefixes stripped, ORCID hyphens normalized.
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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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  • DEPRECATED, removed in 1.0.0: call get_maison with section "founders" and lang "fr" or "en" instead. Until then this alias still answers exactly as before. 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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  • DEPRECATED, removed in 1.0.0: call get_maison with section "person" and the name or QID in person and lang "fr" or "en" instead. Until then this alias still answers exactly as before. 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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  • 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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  • Fetch a Wikidata entity (item or property) by QID or PID. The fields parameter narrows the upstream fetch, not just the response — asking for labels alone costs a fraction of the whole entity, so name the fields you need. Omit fields for all data; a well-connected item is large enough to overflow, and an oversized entity returns kind: "outline" — the field categories with their byte sizes — instead of the data. Follow its retrieval_notice literally rather than picking from sections yourself — it names a fields set already measured to fit, since category sizes are additive and requesting them all would overflow again; for a category too large to deliver whole (statements or sitelinks on a major item) it names the sibling tool that can narrow it. Q-IDs (e.g. Q76) fetch items; P-IDs (e.g. P31) fetch properties from the correct endpoint automatically. Use wikidata_get_statements for deep claim traversal with label resolution, and whenever an entity's statements are large — its properties parameter selects individual P-IDs, granularity fields does not carry.
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  • Fetch property claims for a Wikidata entity with qualifier and reference detail. Value QIDs are resolved to human-readable labels by default. Use the properties parameter to fetch only specific P-IDs — omitting it returns every statement, and a well-connected item (a country, a major city) carries hundreds of properties: more than fits inline. An oversized set comes back as kind: "outline" — every available P-ID with its byte size, largest first — instead of the statements; re-call with the same id plus properties:[...] naming the P-IDs you want. Designed for fact verification: "what does Wikidata say about this entity's {property}?". Preferred-rank statements are the most current values.
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  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,767 across 1506 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.
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  • "What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns `figi_candidates` to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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  • Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).
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  • Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.
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  • Fetch one landmark’s full record by slug (preferred, e.g. "palace-of-versailles") or exact name: coordinates, founding year and century, worldwide fame rank, photo URL with photographer credit and licence, Wikipedia link and its readership signals (Wikidata sitelinks, Wikipedia pageviews over the last 60 days summed across its largest language editions). Unsure of the slug? Call search_castles first.
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