Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
คำอธิบายจากผู้ทำ (ภาษาอังกฤษตามต้นฉบับ)
Wikidata MCP — wraps Wikidata API (wikidata.org/w/api.php)
สามเรื่องนี้ไม่รวมเป็นคะแนนเดียว เพราะความเห็น การวัด และความนิยม เป็นคนละเรื่องกัน
ตัวนี้ยังไม่ได้เรียบเรียงเป็นภาษาไทย ด้านล่างคือข้อเท็จจริงที่เราอ่านจาก registry
เลือกโปรแกรม AI ที่คุณใช้ แล้วคัดลอกค่าตั้งไปวาง
PIPEWORX_API_KEYค่าที่ติดป้ายความลับคือกุญแจของบัญชีคุณ อย่าส่งให้ใคร และอย่าวางในแชต
ต้องเตรียมก่อน
คุณใช้แอปไหน
เลือกแอปด้านบน เราจำไว้ให้ ครั้งหน้าปุ่มจะขึ้นว่า “ติดตั้งลง …”
ใช้ได้ทั้ง Claude บนเว็บ แอปคอมพิวเตอร์ และมือถือ:
https://gateway.pipeworx.io/wikidata/mcpเปิด Claude Desktop → Settings → Developer → Edit Config แล้ววางค่านี้ในไฟล์ claude_desktop_config.json
{
"mcpServers": {
"wikidata": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-wikidata"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}พิมพ์คำสั่งนี้ใน Terminal
claude mcp add --transport http wikidata https://gateway.pipeworx.io/wikidata/mcpclaude mcp add wikidata --env PIPEWORX_API_KEY='ใส่ค่าของคุณ' -- npx -y @pipeworx/mcp-wikidataเติมค่าที่เว้นว่างไว้ก่อนรัน
วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json
{
"mcpServers": {
"wikidata": {
"url": "https://gateway.pipeworx.io/wikidata/mcp"
}
}
}วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json
{
"mcpServers": {
"wikidata": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-wikidata"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
วางในไฟล์ .vscode/mcp.json ของโปรเจกต์
{
"servers": {
"wikidata": {
"type": "http",
"url": "https://gateway.pipeworx.io/wikidata/mcp"
}
}
}วางในไฟล์ .vscode/mcp.json ของโปรเจกต์
{
"servers": {
"wikidata": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-wikidata"
],
"env": {
"PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
}
}
}
}เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง
ChatGPT ต่อได้เฉพาะ server ที่อยู่บนอินเทอร์เน็ต:
https://gateway.pipeworx.io/wikidata/mcpชื่อเมนูอาจต่างกันเล็กน้อยตามแพ็กเกจและรุ่นของแอป
ตัวเลือกเก็บในคุกกี้บนเครื่องนี้ ไม่ต้องมีบัญชี
ค่าตั้งนี้สร้างจากข้อมูลใน registry ของผู้ทำ ถ้าไม่ตรงกับ README ให้เชื่อ README
ชื่อและคำอธิบายเครื่องมือตามที่ server ตอบมาเมื่อ 7 พ.ย. 2569 ผู้ทำเป็นคนเขียน ไม่ใช่เรา แสดงเป็นข้อความล้วน
ask_pipeworxPREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring au
ask_pipeworx_betaBeta version of ask_pipeworx: identical universal router (same 7,181 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so
ask_pipeworx_groundedHallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,181 across 1864 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbati
search_withinSemantic 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 th
deep_researchACCOUNT 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 1864 STRUCTURED data sources (SEC filings,
discover_toolsFind 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 est
resolve_entity"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 re
compare_entities"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
subscribeCreate 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"] = o
unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
recent_alertsPull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp).
entity_profile"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over cha
recent_changes"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 s
validate_claim"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is f
company_factsTYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the pe
company_register_searchSearch the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want
scan_dependencyComposite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular
bet_researchResearch a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it sett
polymarket_arbitrageFind 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. `ev
polymarket_edgesScan 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_segme
polymarket_kalshi_spreadCross-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 s
polymarket_fill_riskRealizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the di
polymarket_edge_trackerEdge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: d
kalshi_weather_edgePrices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob
resolution_auditExtract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. P
resolution_diffField-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket .com, Polymarket US, or Kalshi) — runs resolution_audit on both sides and compares source, settle time, precision, comparison_basis, and evidence standard. Returns `equi
release_calendar_marketsJOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are c
prediction_calibrationREALIZED win rate by shown price and minutes elapsed for short-horizon crypto up/down markets, net of taker fee — read from our own settled-outcomes archive, not from live prices. Answers the one question no venue will: "a side shown at 82 with five minutes left, how often did it
updown_venue_compareThe same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as
updown_divergence_rateHow often two venues settled the SAME 15-minute crypto window differently, over every settled window on record. Compares polymarket.com (Chainlink TWAP) with Kalshi and with Polymarket US (both CF Benchmarks BRTI), and Kalshi with Polymarket US as the same-feed control. Per pair:
archive_evidenceWhat did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy
archive_compareWhat changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and b
research_rowClaygent-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 in
classify_rowCheap 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, e
pipeworx_trendingWhat 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
suggest_questionsWhat can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed e
generate_llms_txtGenerate 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 si
ai_visibility_checkProbe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns pe
scan_competitor_ai_presenceCompare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our co
pipeworx_feedbackTell 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 Pipewor
rememberSave 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-v
recallRetrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous
forgetDelete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
search_entitiesSearch Wikidata entities by label or alias (e.g., "Albert Einstein", "Python programming language", "Tokyo"). Returns entity IDs, labels, descriptions, and aliases. Useful for finding the Wikidata ID of any concept.
get_entityGet full Wikidata entity by ID (e.g., "Q42" for Douglas Adams, "Q5" for human, "Q1764" for Budapest). Returns labels, descriptions, aliases, claims/statements (properties and values), sitelinks, and the revision the data came from — lastrevid plus the modified timestamp — so live
get_entitiesBatch version of get_entity — resolve up to 50 Wikidata Q-ids in ONE call instead of looping single get_entity calls. Same fields as get_entity per entity (labels, descriptions, aliases, claims, sitelinks, revision info), keyed by Q-id. Any id that does not resolve (bad format, d
get_wikidata_factsStructured facts about a Wikidata entity in HUMAN-READABLE form — property names and values resolved to labels, not raw P/Q codes. PREFER OVER get_entity for "what is X's ", "facts about X", "X's date of birth / capital / population". E.g. Q42 (Douglas Adams) -> {"date of birth":
wikidata_recent_changesNewest items created or edited on Wikidata right now, from the live MediaWiki recent-changes feed. Answers "which Wikidata item was created most recently", "what QIDs were created today", "what changed on Wikidata in the last hour", "recent edits to Wikidata items". Returns each
เราตรวจจากข้อมูลที่ใครก็เปิดดูได้ ยังไม่ได้อ่านโค้ด ข้อที่ไม่เกี่ยวกับตัวนี้ไม่นับในตัวหาร · ตรวจเมื่อ 7 พ.ย. 2569
ชื่อใน registry ยืนยันด้วยบัญชี GitHub pipeworx-io ซึ่งเป็นเจ้าของ repo
ยังไม่ได้อ่านข้อมูล repo จาก GitHub
ยังไม่ได้อ่านข้อมูล repo จาก GitHub
ตอบกลับใน 639 ms (HTTP 200)
ไม่ต้องล็อกอิน
อ่านคำอธิบายเครื่องมือ 49 ตัวแล้ว ไม่พบคำสั่งแอบแฝงถึง AI
เทียบกับทุก server ที่ยังใช้งานในหมวดนี้ทั้งทะเบียน ดาวนับเฉพาะเมื่อผู้ลงทะเบียนเป็นเจ้าของ repo
จากการตรวจเบื้องต้นของเรา ผ่าน 3 จาก 3 ข้อที่ตรวจได้ เรายังไม่ได้อ่านโค้ด ก่อนใส่กุญแจหรือ token ให้ดูว่า server ขอสิทธิ์อะไร และเลือกกุญแจที่ทำได้เท่าที่จำเป็น
ต้องใส่ค่าเหล่านี้: PIPEWORX_API_KEY
ได้ทั้งสองแบบ จะลงในเครื่องหรือต่อผ่านคลาวด์ก็ได้
repo บน GitHub มีการแก้โค้ดล่าสุด – และส่งรุ่นล่าสุด (0.2.1) เข้า registry วันนี้
โปรแกรมที่รองรับ MCP เช่น Claude Desktop, Claude Code, Cursor และ VS Code ตัวนี้ต่อผ่าน URL จึงใช้กับโปรแกรมที่รับ remote MCP ได้ เช่น ChatGPT ที่เปิดโหมดนักพัฒนา
ในหมวดเดียวกันมี Spec Driven Development, Remote Desktop Commander, MemberVault MCP by usefulapi
server หมวดเดียวกันจากผู้ทำคนอื่น เรียงตามดาว
อ่านการ์ด:★ ดาว GitHubiสามเสา: รีวิว · ผลตรวจ · การใช้งานiอัปเดต = วันที่ส่งรุ่นล่าสุดเข้า registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
List courses; add users to courses, remove course access or delete users in MemberVault.
ส่วนนี้คัดจาก repo ของผู้ทำตามต้นฉบับ PopNx MCP ไม่ได้ตรวจหรือรับรองเนื้อหา ตัด HTML รูปภาพ และสคริปต์ออกแล้ว ลิงก์จะพาออกนอกเว็บ
Wikidata is the free, structured-data sister of Wikipedia. ~110 million items (people, places, works, concepts) with machine-readable properties and relationships. The closest thing the open web has to a universal knowledge graph. Free, no auth, supports SPARQL queries.
Part of Pipeworx — an MCP gateway connecting AI agents to 1867+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.
For entity disambiguation, structured facts about anything notable, or graph traversal ("what books did this author write that won prizes?"), Wikidata is the canonical open source. Pair with Wikipedia (prose) and OpenAlex (academic specifics) for full open-knowledge coverage.
Common flows:
| Tool | What it returns |
|---|---|
search_entities | Q-ids matching a label or alias, with labels, descriptions and aliases. Start here when you have a name and need an id. |
get_entity | Full entity by Q-id — labels, descriptions, aliases, raw P-coded claims, sitelinks, plus lastrevid / modified. Takes language. |
get_entities | The same, for up to 50 Q-ids in ONE call. Returns a per-QID map plus a not_found list, so one bad id doesn't fail the batch — see "One bad id used to zero the batch" below. Takes language. |
get_wikidata_facts | The same statements with property names and values resolved to human-readable labels, plus lastrevid / modified. Prefer this for "what is X's ". Takes language. |
wikidata_recent_changes | Items created or edited most recently, newest-first, from list=recentchanges — Q-id, revision ids, UTC timestamp, user, comment, byte size, plus a cursor for paging further back. |
search_entities, get_entity, get_entities and get_wikidata_facts all take language. It is passed straight to the API's own languages param, so a single code ("ka") or a comma/pipe-joined list ("ka,ru,en") both work, and a list returns every requested language's label, description and aliases side by side in one upstream call — the shape you need to decide whether two records in different languages are the same entity. Omit it and you get English, exactly as before.
Two fields, and the difference matters:
labels / descriptions / aliases_by_language are keyed by language and contain only what Wikidata actually has. Ask for a language the entity has no label in and the key is simply absent — that absence is the honest answer, and it is how you detect a translation gap.label / description are the single best value, and they fall back to English when the requested language is missing. So get_entity({id:"Q162887", language:"zu"}) returns label: "Enguri HPP" with an empty labels map: there is no Zulu label, and you can tell.get_wikidata_facts localizes both halves — property names and their values — so {id:"Q162887", language:"ka"} comes back with Georgian keys (ქვეყანა → საქართველო), not English keys with Georgian values.
`mul` is a real language code here, and you will see it. Wikidata moved language-neutral labels — personal names, most Latin binomials, many place names — to the mul ("multiple languages") code, and wbgetentities then returns an *empty* en label map for those items. Q42 (Douglas Adams) is one: it has an English description and no English label. The fallback chain is therefore requested → `en` → `mul`, and when the name came from mul you see it under a mul key in the labels map:
{"id":"Q42","label":"Douglas Adams","labels":{"mul":"Douglas Adams"},
"description":"British science fiction writer and humorist (1952–2001)"}
That key is the provenance: the name is not attributed to a language the entity does not carry. Before this the pack returned label: null for Q42 — a clean 200 with a null where the answer was.
wbgetentities is inconsistent about unknown ids, and the two cases look nothing alike:
Q999999999) comes back marked missing, and every other id in the request still resolves.Q99999999999, NOTAQID) makes the API refuse the entire request — HTTP 200, no entities at all, a top-level {"error":{"code":"no-such-entity","id":"Q99999999999"}}.Read naively the second case is an empty map, so one typo in a 50-id batch reported all 50 as not_found: a clean 200 saying nothing resolved. The error names the offending id, so get_entities drops it and retries (bounded, one id per pass). A batch of ["Q162887","Q99999999999","Q90","NOTAQID","Q1326165"] now resolves the three real entities and returns the two bad ids in not_found.
Wikidata is live and continuously edited, so "the current statements" is only meaningful relative to a revision. Both entity tools return lastrevid, modified and a revision_url, so an answer built from them can be pinned to the exact edit it was read at, and re-checked later for drift.
wikidata_recent_changes answers the other half of that: which items changed, and when. Namespace 0 is items (Q-ids), 120 is properties (P-ids), 146 is lexemes. type is new (freshly created), edit (edits to existing items) or all. Use since for a time window and cursor to page further back.
None. Wikidata is fully open. Wikidata's SPARQL endpoint has fair-use rate limits but generous for most agent traffic.
| Prefix | Type |
|---|---|
| Q | Item (an entity — person, place, thing) |
| P | Property (a relationship type — "spouse," "instance of," "located in") |
| L | Lexeme (a word/sense, for linguistic data) |
Every entity has a stable Q-number that serves as a permanent identifier across Wikipedia language editions. Embed Q-numbers in agent output as canonical citations.
references field on properties you're acting on.references chain to an authoritative source.label falls back to English, then to the language-neutral mul label, when that happens, so a non-English request never comes back empty — but it also means a returned label is not proof the entity HAS a label in that language. Read the labels map for that: a missing key is a real gap, and a mul key means the name is language-neutral rather than translated. See "Reading labels in another language" above.https://www.wikidata.org/w/api.phpaction=wbsearchentities (search), action=wbgetentities (entity read, props=info|labels|descriptions|aliases|claims|sitelinks, languages= for the language filter, up to 50 pipe-joined ids per call)action=query&list=recentchanges (recent changes feed)https://www.wikidata.org/wiki/ ; a specific revision: https://www.wikidata.org/w/index.php?oldid= All endpoints are keyless. Wikidata asks callers to send a descriptive User-Agent; this pack sends Pipeworx-Wikidata-MCP/1.0.
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": { "wikidata": { "url": "https://gateway.pipeworx.io/wikidata/mcp" } } }
tools/list at https://gateway.pipeworx.io/wikidata/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": { "pipeworx": { "url": "https://gateway.pipeworx.io/mcp" } } }
Both URLs reach the same gateway and the same 1867+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.
curl -X POST https://gateway.pipeworx.io/v1/tools/wikidata_search_entities \
-H 'Content-Type: application/json' \ -d '{"query":"Albert Einstein"}'
No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/wikidata_search_entities. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.
@pipeworx/mcp-wikidata is a local stdio bridge to the Pipeworx gateway: your MCP client starts it with npx, and it forwards every call to the same https://gateway.pipeworx.io/wikidata/mcp endpoint shown above. It needs network access; there is no offline mode.
{
"mcpServers": { "wikidata": { "command": "npx", "args": ["-y", "@pipeworx/mcp-wikidata"] } } }
Without a key it calls anonymously, capped at 50 calls a day. A free account raises that to 200 a day: get a key at https://pipeworx.io/signup and add "env": { "PIPEWORX_API_KEY": " " } to the server entry. The key is sent
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