原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Wikidata

作者:pipeworx-io版本 0.2.1i★ –i电脑或云端
注册名称io.github.pipeworx-io/wikidata

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 电脑或云端
  • 传输方式: stdio, Streamable HTTP
  • 分类: 其他
  • 你需要填写:PIPEWORX_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • 在云端运行无需安装任何东西,通过互联网连接
  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://gateway.pipeworx.io/wikidata/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "wikidata": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-wikidata"
      ],
      "env": {
        "PIPEWORX_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add --transport http wikidata https://gateway.pipeworx.io/wikidata/mcp

或者在你的电脑上运行

claude mcp add wikidata --env PIPEWORX_API_KEY=your-value -- npx -y @pipeworx/mcp-wikidata

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "wikidata": {
      "url": "https://gateway.pipeworx.io/wikidata/mcp"
    }
  }
}

或者在你的电脑上运行

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "wikidata": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-wikidata"
      ],
      "env": {
        "PIPEWORX_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "wikidata": {
      "type": "http",
      "url": "https://gateway.pipeworx.io/wikidata/mcp"
    }
  }
}

或者在你的电脑上运行

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "wikidata": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-wikidata"
      ],
      "env": {
        "PIPEWORX_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

ChatGPT 只能连接互联网上的服务器:

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://gateway.pipeworx.io/wikidata/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • PIPEWORX_API_KEY 机密 env
    Optional Pipeworx API key. Without it calls are anonymous and capped daily; a free key raises the cap.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月8日 报告的完全一致。这些内容由作者撰写,不是我们写的;我们以纯文本形式显示。

  • ask_pipeworx

    PREFER 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_beta

    Beta version of ask_pipeworx: identical universal router (same 7,188 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_grounded

    Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,188 across 1866 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbati

  • search_within

    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 th

  • deep_research

    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 1866 STRUCTURED data sources (SEC filings,

  • discover_tools

    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 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

  • subscribe

    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"] = o

  • unsubscribe

    Cancel 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_subscriptions

    List 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_alerts

    Pull 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_facts

    TYPED, 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_search

    Search 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_dependency

    Composite "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_research

    Research 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_arbitrage

    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. `ev

  • polymarket_edges

    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_segme

  • polymarket_kalshi_spread

    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 s

  • polymarket_fill_risk

    Realizable-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_tracker

    Edge 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_edge

    Prices 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_audit

    Extract 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_diff

    Field-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_markets

    JOIN 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_calibration

    REALIZED 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_compare

    The 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_rate

    How 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_evidence

    What 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_compare

    What 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_row

    Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code in

  • classify_row

    Cheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, e

  • pipeworx_trending

    What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice

  • suggest_questions

    What 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_txt

    Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at si

  • ai_visibility_check

    Probe 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_presence

    Compare 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_feedback

    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 Pipewor

  • remember

    Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-v

  • recall

    Retrieve 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

  • forget

    Delete 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_entities

    Search 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_entity

    Get 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_entities

    Batch 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_facts

    Structured 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_changes

    Newest 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

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 pipeworx-io 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • ✓ 通过

    端点有响应

    在 102 毫秒内响应(HTTP 200)

  • – 不适用

    使用 OAuth 登录

    无需登录

  • ✓ 通过

    工具描述中没有隐藏指令

    已读取 49 个工具描述,未发现针对 AI 的隐藏指令

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 18444 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 18444 个服务器的 97% 更新
  • 基础检查结果等于或优于此分类中其他 18444 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Wikidata 安全吗?

它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:PIPEWORX_API_KEY

它在我的电脑上运行还是在云端?

两种方式都可以:安装在你的电脑上,或者连接云端版本。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.2.1)于昨天进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Tethered Trust

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

作者:formulahendry起步阶段↓ 66/周★ 44014个月前更新

在你的电脑上运行

Remote Desktop Commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

作者:desktop-commander正在增长★ 2113天前更新

在云端运行✓ desktopcommander.app

Tethered Trust

Fit, compatibility and recall checks for AI agents before they buy: verdicts, evidence, receipts.

作者:tetheredtrust.com非常新今天更新

在云端运行✓ tetheredtrust.com

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Wikidata — Structured Knowledge Graph

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.

Why this matters for AI agents

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:

  • Entity lookup. "Who is Paul McCartney?" → Q2599 with structured facts (birth date, nationality, occupation, band memberships).
  • Property query. "What's the population of France?" → Q142, property P1082 (population), with references and timestamps.
  • Disambiguation. "Paris" → Q90 (capital of France), Q830149 (Paris, Texas), Q3296 (Paris, Greek mythology). Wikidata's structured types resolve which "Paris" the agent is asking about.
  • Graph traversal. SPARQL: "All Nobel laureates in Physics born in Italy" → joinable across structured properties.

Tools

ToolWhat it returns
search_entitiesQ-ids matching a label or alias, with labels, descriptions and aliases. Start here when you have a name and need an id.
get_entityFull entity by Q-id — labels, descriptions, aliases, raw P-coded claims, sitelinks, plus lastrevid / modified. Takes language.
get_entitiesThe 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_factsThe 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_changesItems 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.
Reading labels in another language

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.

One bad id used to zero the batch

wbgetentities is inconsistent about unknown ids, and the two cases look nothing alike:

  • An unknown but well-formed id (Q999999999) comes back marked missing, and every other id in the request still resolves.
  • An id outside the item-id range or plainly malformed (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.

Pinning statements to an edit

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.

Auth

None. Wikidata is fully open. Wikidata's SPARQL endpoint has fair-use rate limits but generous for most agent traffic.

Identifier scheme

PrefixType
QItem (an entity — person, place, thing)
PProperty (a relationship type — "spouse," "instance of," "located in")
LLexeme (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.

Common pitfalls

  • Quality varies wildly by entity. A famous person (Einstein, Q937) has hundreds of well-sourced properties. A small-town politician may have 5 properties, half from auto-imported sources. Always check the references field on properties you're acting on.
  • Multiple values for "current" properties. "President of company X" may have 8 historical values plus a current one. Wikidata uses qualifier properties (start time, end time, "preferred rank") to indicate which is current — but agents often grab the first value naively.
  • Property duplication. Multiple properties can express related concepts ("country" P17, "country of origin" P495, "country of citizenship" P27). Picking the right one matters for accuracy.
  • Vandalism risk. Wikidata is editable like Wikipedia. High-profile entities are watched, but obscure ones can carry vandalism for days. Don't quote a single Wikidata fact as ground truth without a references chain to an authoritative source.
  • Translation gaps. Labels exist in many languages but not always in the one you asked for. 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.
  • SPARQL timeouts. Complex graph queries can hit the SPARQL endpoint's 60-second timeout. Decompose into smaller queries or use property-specific lookups.
  • Identifiers, not facts. Wikidata is best for "what's the canonical ID of this entity" and "what does it link to." For deep biographical or historical narrative, follow the linked Wikipedia article.

Data sources

  • MediaWiki Action API — https://www.wikidata.org/w/api.php
  • action=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)
  • API docs: https://www.wikidata.org/w/api.php and https://www.mediawiki.org/wiki/API:Recentchanges
  • Entity pages: 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.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{

"mcpServers": { "wikidata": { "url": "https://gateway.pipeworx.io/wikidata/mcp" } } }

What this endpoint actually serves

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.

No MCP client? Call it over HTTP

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":"..."}.

Run it from npm

@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

只显示了开头部分;其余内容请在仓库中阅读

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