io.github.mrpkk由 GitHub 帳號 mrpkk 發布v0.7.1更新於 Nov 4, 2026
Proof-of-spend and reserve checks for AI agents: receipts, spend audit, NAV, reconciliation.
發布方自己的服務：Proof Holdings
驗證 AI 代理的簽章支出憑證，並稽核代理花費、儲備淨值、結算證據與發票對帳。
Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。
把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。
閱讀 https://sourceweft.com/mcp/io.github.mrpkk%2Fnidhipati/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。把它新增到你客戶端的 mcpServers 設定中。
{
"mcpServers": {
"nidhipati-proof-of-spend-for-ai-agents": {
"type": "http",
"url": "https://rta.lat/nidhipati/mcp"
}
}
}Proof of Spend for AI agents. A signed receipt that proves what an agent was authorized to pay, what it actually paid, what it used and what was refunded. Not a token, not custody, not another payment rail.
Рельсы двигают деньги. Nidhipati доказывает, что движение было правильным.
[tests] [MCP Registry] [Glama] [license]
Status: v0.6 — five CLIs, an MCP server, 94 tests, official MCP Registry and Glama listing.
Site: https://mrpkk.github.io/nidhipati/
Free browser audit (no signup): https://mrpkk.github.io/nidhipati/free-spend-audit.html
BusinessMCP directory: https://businessmcp.com/mcp-servers/nidhipati
Private scan request: https://github.com/mrpkk/nidhipati/issues/new?template=free-spend-audit.yml
For tokenised assets: the issuer's own numbers turned into a NAV with explicit formulas and an
evidence hash per line. The verdict is backed, underbacked, or incomplete — and the ratio
is refused when any line has no price, because a number computed over half a reserve is not
a number.
The statement hash is compared with the file if you provide it. The disclaimer travels inside the signed document. Details and the partner/legal checklist: docs/RESERVE-REPORT.md.
When a market resolves and someone disagrees, the question is whether the resolver's sources
say what the resolver claims and whether they agree with each other. The report answers with
four verdicts — supported, disputed, single_source, insufficient — and never picks a
winner.
Details: docs/RESOLUTION-EVIDENCE.md.
Invoices on one side, transfers on the other. The report names what is closed, what nobody paid, and what arrived without an invoice — a transfer that duplicates a payment is counted once, never twice.
Details: docs/RECONCILIATION.md.
The same five checks over the Model Context Protocol: verify_receipt, audit_spend,
reserve_report, resolve_report, reconcile_books. Input is data, never a file path and never
a network call; the answers are the same reports the CLIs produce, including the refusal to print
a backing ratio when a line has no price.
Five tools, all read-only: no custody, no keys, no chain read.
Official MCP Registry: https://registry.modelcontextprotocol.io/v0/servers?search=nidhipati (io.github.mrpkk/nidhipati, OCI ghcr.io/mrpkk/nidhipati:0.6.0).
Glama listing: https://glama.ai/mcp/servers/mrpkk/nidhipati.
Send records of what your agents spent and get the audit back — no card. The browser audit needs no account; a human review can go by email or Telegram, no GitHub needed: email [email protected] or Telegram with the downloaded report (not raw rows). GitHub is optional: open a private scan request; the issue is public, so ask the question only and never paste records or keys. Paid: first 3 audits $1,250; then $2,500 fixed, $1,500/mo retainer, or 20% of verified savings. $5,000 finding guarantee (identified overcharges, not recovery). Full terms, including what is not included: docs/OFFER.md.
In CI, on every run:
usage.quantity × usage.unit_price vs payment;allow for a settled payment;A signed receipt proves one charge. A bill proves a month. The audit reads the records you already have (CSV or JSON) and names the rows where money left without a reason:
Exit code 1 when there is an error — a pipeline can fail on it. The report keeps three
amounts apart on purpose: spent without a reason (duplicates, escalation, failed calls,
refunds not returned), above the usual price (an estimate, not proof), and over the
limit (the money may have been spent on real work).
The sample is synthetic and labelled: it contains every finding once, so you can see what a report looks like before trusting it with real invoices.
onchain_verified=false is explicit.SPEC.md — product, architecture, modules, SEO, monetization, 30/60/90.PROGRESS.md — slices, acceptance, metrics.docs/PROMPT-AND-IDEAS.md — prompt and decisions for RAG.docs/SEO-MONETIZATION.md — keywords, pages, funnel, pricing.docs/LAUNCH-KIT.md — Show HN, Reddit, Product Hunt, X/LinkedIn copy.docs/DEMO.md — real commands and sample output.docs/AGENT-SPEND-AUDIT.md, docs/RESERVE-REPORT.md, docs/RESOLUTION-EVIDENCE.md, docs/RECONCILIATION.md — module rules.schemas/proof-of-spend.schema.json — v0.1 schema.examples/proof-of-spend.example.json — example.Apache-2.0.
來源：README.md，提交 2c3d835
Verify an agent payment receipt
Check a signed proof-of-spend receipt offline: schema, Ed25519 signature, mandate expiry, positive amount, quantity*unit_price == amount, refund not above payment. Use when an agent or provider returns a receipt and you need to know whether it actually proves the charge was allowed. Do NOT use to move funds or to confirm a transaction on-chain - nothing here reads a chain.
receipt必填public_key必填Audit an agent's spending records
Find duplicates, price escalation, failed-but-billed calls, runaway loops, missing refunds, model overpay, unknown asset/network and budget breaches in a list of settled agent calls. Returns three separate amounts (money without a basis, estimate above the agent's own median, budget overrun) instead of one sum, because adding them together would overstate what can be recovered. Do NOT use as a statement of money actually returned.
policyrecords必填Reserve and NAV report for a tokenised asset
Turn an issuer's reserve lines into a NAV with explicit formulas and a sha256 evidence hash per line. Verdict is backed, underbacked, or incomplete - and the backing ratio is refused (null) when any line has no price, because a ratio over half a reserve is not a number. Do NOT treat the result as a statutory audit.
reserve必填Resolution evidence for a prediction market
Weigh the sources a resolver says it read against the declared outcome. Verdicts: supported, disputed, single_source, insufficient. A conflict is reported as a conflict, never averaged. Do NOT use to trade or to place a position.
market必填Reconcile invoices against transfers
Match invoices to stablecoin transfers by memo first, then exact amount and asset inside the invoice window. Names what is left over in both directions: invoices nobody paid, money nobody invoiced, invoices paid twice. A duplicate is counted once, never twice. Do NOT use as proof of who the payer was.
invoices必填transactions必填已滿足適用檢查項的 100%
該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。
holdings.proof官方發布者
Proof Holdings MCP 伺服器讓助理發起並管理身分與控制權驗證（手機、電子郵件、網域、錢包），管理證明權杖與委派，並進行驗證。
由 GitHub 帳號 kivtxs 發布
讓助理閱讀 Proof of Work 的規則、檢查本機是否已有帳號，並建立代理帳號以發佈、投票與支持附證據的主張。
由 GitHub 帳號 frankjie3-create 發布
QuietProof 提供免費探索工具，用來瀏覽一組確定性、唯讀的稽核服務，這些稽核按次以 Base 上的 USDC 透過 x402 付費。
com.scriptmasterlabs
遠端 MCP 端點，用於在 XRPL 上以 RLUSD 建立、驗證與評分發票，並對結算進行自證。
由 GitHub 帳號 ZKStamp 發布
遠端 MCP 伺服器，把 SHA-256 指紋以遮蔽交易寫入 BitcoinZ 區塊鏈，產生附日期的存在性證明時間戳。
nidhipati-reserve report examples/reserve.example.json --key keys/private.pem --markdown report.mdNAV = Σ quantity × price × (1 − haircut) − liabilitiesratio = NAV / supplynidhipati-resolve report examples/resolve.example.json --key keys/private.pem --markdown report.mdnidhipati-reconcile run examples/invoices.example.csv examples/transactions.example.csv --csv matches.csv{"jsonrpc":"2.0","id":1,"method":"tools/call", "params":{"name":"reserve_report","arguments":{"reserve":{...}}}}python3 -m nidhipati_proof.mcp_stdio # stdio transportdocker build -t nidhipati-mcp . && docker run -i --rm nidhipati-mcp- uses: mrpkk/[email protected] with: records: billing/agent-spend.csv policy: billing/policy.jsonpython3 -m pip install -e .[dev]python3 -m pytest -qpython3 -m nidhipati_proof.cli verify examples/proof-of-spend.example.json --pubkey <base64url>python3 -m pip install -e .nidhipati-spend audit examples/spend-sample.csv --policy examples/spend-policy.example.jsonnidhipati-spend audit records.csv --json > report.json