Stipple — Reference Verification

sh.stipple由 stipple.sh 发布官方发布者v0.1.5更新于 Oct 25, 2026

Fact-check citations: resolve, match, support claims. Arithmetic rechecked. Free to start.

发布方自己的服务：Stipple - Government Tenders and Grants

已验证Streamable HTTP可网页运行网页搜索与抓取知识与记忆

服务详情

标识符: sh.stipple/openwarrant-verify
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此服务的命名空间属于 stipple.sh，且该域名同时运行其端点。这可以确认发布者，但并不代表该服务的工具是安全的。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/sh.stipple%2Fopenwarrant-verify.svg)](https://sourceweft.com/mcp/sh.stipple%2Fopenwarrant-verify?utm_source=readme&utm_medium=badge)

概览

AI 生成的概览

Stipple 托管的 MCP 服务器，其 verify_references 工具可核查报告的引用与论断，检查参考文献能否解析、是否匹配并支撑所述内容。

可以做什么: 核查文档的参考文献与论断：检查引用能否解析、是否与来源匹配并支撑相应论断，标记缺乏支持的论断和无法重新计算的算术，并报告核查覆盖范围而非真伪结论。
适用场景: 当有人分享 AI 生成的报告、文章、白皮书或深度研究导出内容，并询问其引用是否真实或内容是否准确时使用，尤其适合在采信或转载之前核查。
运行要求: 远程 Streamable HTTP 端点；无需安装或注册，匿名免费额度可立即使用。可选免费 API 密钥通过 Authorization Bearer 请求头发送，以获得独立额度。端点须使用 www 主机。

安装前请注意

该工具报告的是核查覆盖范围而非真伪结论，结果应视为信号而非确认。提交的文档或链接会发送给由 Stipple AI Pty Ltd 运营的第三方托管服务，超出免费额度后付费调用可能消耗额度。

安装

在 SourceWeft 中

打开控制台中的 Stipple — Reference Verification，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/sh.stipple%2Fopenwarrant-verify/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "stipple-reference-verification": {
      "type": "http",
      "url": "https://www.stipple.sh/mcp-verify"
    }
  }
}

README

Stipple MCP server

Documents in. Answers your agents can act on.

Stipple is a hosted MCP server for document work: forensic authenticity signals, structured field extraction, Australian identity checks, adverse-media and sanctions screening, AU/NZ government tender search, AI-written-text detection, and citation verification for research reports.

No install, no signup — an anonymous free tier works immediately.

Connect

Or in any MCP client config:

Endpoint: https://www.stipple.sh/mcp (Streamable HTTP)
Always the www. host — the apex domain redirects, and a POST through the redirect fails for non-browser clients.
Auth: none required. An optional free API key (POST /v1/keys) gives a private credit allowance; send it as Authorization: Bearer stp_....

Tools (13)

Tool	What it does	What it is NOT
`verify_document`	Forensic authenticity inspection of a PDF/image (URL or base64): `risk_band` + `inspection_quality` + evidence	A fraud verdict — it is a signal with the evidence behind it
`extract_fields`	Structured fields from a document: ad-hoc field lists, templates (payslip, tax invoice, bank statement, receipt, contract), tables, per-value grounding, redaction, layout, chunking, splitting	A judgment — values are what the document shows; absences come back in `not_found`
`verify_identity`	Australian identity check over a document set: AFP 100-point or AUSTRAC safe-harbour, with exactly what is missing	A forgery check — pair with `verify_document`
`check_pack`	Does a document set satisfy a checklist (named scheme or ad-hoc requirements)?	Approval — presence of the right types, not genuineness
`screen_adverse_media`	Adverse media + corroboration-gated sanctions/PEP screening of a person or organisation	A determination — "review", never "guilty"; "nothing found" is not a clean record
`find_tenders`	Free. Search open AU/NZ government tenders	—
`match_tenders`	Rank open tenders against what a company's own website says it does, with `why[]` and `gaps[]`	A win probability
`tender_sources`	Free. Where tender data comes from, and why a search can be empty	—
`detect_ai_text`	Probability a document's prose is AI-written, with the linguistic tells; abstains on non-prose	Calibrated truth — and style, not authenticity
`verify_references`	For a report: citations resolve and match, internal arithmetic recomputes, unsupported claims flagged	A truth verdict — it reports verification coverage
`check_document`	Has this exact file (sha256) been inspected already? Skips a paid call	—
`get_warrant`	Fetch a stored result bundle (JSON or Markdown)	—
`submit_feedback`	Thumbs up/down on a rating	—

Prompts (8)

Guided workflows shipped with the server: Verify a document's authenticity · What kind of document is this? · Run a 100-point identity check · Screen a person for adverse media · Find tenders my company could bid for · Was this written by AI? · Fact-check a report URL · Fact-check pasted text.

How results read

Every inspection reports two independent axes: risk_band (how authentic the document looks) and inspection_quality (how completely it could be inspected). A clean phone photo of a real payslip is commonly low risk + limited quality — "nothing looks tampered, but we couldn't read everything". Low coverage is not risk.

Identical documents are cached by content hash — resubmitting the same bytes returns the stored result instantly and free.

Credits

Anonymous callers share a free weekly allowance per IP. A free API key gets its own allowance and metering. Tender search and source listing cost nothing. Details: stipple.sh/pricing.

Also available

REST API — the same engines at /v1/*; OpenAPI contract at /openapi.json
Agent docs — /agents.md (short form /llms.txt)
Scoped servers — /mcp-aitext (AI-text detection only) and /mcp-verify (reference verification only), for installs that want a single tool

Operated by Stipple AI Pty Ltd · Privacy · Terms

来源：README.md，提交 b0cc70c

工具

verify_references

Verify a report's citations

Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted. USE THIS WHEN someone shares a report, article, whitepaper, or deep-research export (or a link to one) and asks: is this accurate / legit? are these citations real? fact-check this. did the AI make this up? Also use it proactively before relying on any AI-written document. Provide the document ONE way: `url` (a public http(s) link to a PDF or web page — fetched server-side, the cheapest call: no need to download or encode anything), `text` (pasted markdown/plain prose), OR `bytes_b64` (a base64 PDF; URLs are read from the PDF's link annotations, so they're exact). Default (fast): provenance (is it a ChatGPT deep-research export?), citation resolution (live / archived / dead, papers matched against arXiv/Crossref to catch 'real ID, wrong paper'), and internal MATH (recompute the doc's own arithmetic). Set `deep=true` to also fetch each cited source and judge whether it SUPPORTS or CONTRADICTS the claim (slower, ~a minute). Returns a trust summary, per-item tables, and a shareable `permalink` to the public fact-check record. HONEST BOUNDARY: this reports verification COVERAGE, not truth — 'supported' means evidence-backed (not necessarily true) and 'unsupported' means no evidence found (not necessarily false). It tells a reviewer WHERE to look; it does not bless the document, and it never affects the fraud risk band.

输入参数urldeeptextfilenamebytes_b64