nidhipati — proof of spend for AI agents
OCI · GHCR.IO/MRPKK/NIDHIPATI · 2 COMPONENTS
Proof-of-spend and reserve checks for AI agents: receipts, spend audit, NAV, reconciliation.
Available components
Last scanned (8 hours ago)
Scores higher than 53% of OCI servers
Category Finance and Payments
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security0
- No malware scan is available for this kind of package: the supply-chain vendors we use do not cover it. This is a permanent gap in our coverage, not a finding about the package.Unverified
- Known CVEs could not be checked: this artifact ships no SBOM or dependency manifest, so there is no dependency list to read.Unverified
- Install-script risk not yet assessed.Unverified
- Dependency health could not be checked: this artifact ships no SBOM or dependency manifest, so there is no dependency list to read.Unverified
Provenance & Transparency32
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: no license is declared. See how to fix → Fail
- Actively maintained (last published 11 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability75
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 544 tokens (~108/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management40
- Stability observed for 12 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities60
- Spec-recency check failed: implements MCP spec 2025-06-18; the latest is 2026-07-28. See how to fix → Fail
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.
- 2 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.
- 31 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.
- 29 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.
- 27 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.
- 25 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 0 to 3. That category is still filling its 30-day observation window: 0 days of observed history at the previous scan, 1 at this one. The score rises as the window fills, whether or not the server changes.
- 24 Oct 26 36
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed oci/ghcr.io/mrpkk/nidhipati@0.6.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | oci |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
audit_spend Audit an agent's spending records ~152
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.
| Name | Type | Req | Description |
|---|---|---|---|
| policy | object | – | Optional budget policy: {"limit_usd": "100"}. |
| records | array | yes | Rows with ts, agent_id, tool, quote, settled, status; optional model, actual, refund, asset, network, tx_hash, payee. |
No output schema declared.
No examples provided.
reconcile_books Reconcile invoices against transfers ~114
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.
| Name | Type | Req | Description |
|---|---|---|---|
| invoices | array | yes | id, amount, asset, created_at; optional due_at, payee, memo. |
| transactions | array | yes | tx_hash, amount, asset, timestamp; optional to, memo. |
No output schema declared.
No examples provided.
reserve_report Reserve and NAV report for a tokenised asset ~87
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.
| Name | Type | Req | Description |
|---|---|---|---|
| reserve | object | yes | Reserve document. |
No output schema declared.
No examples provided.
resolve_report Resolution evidence for a prediction market ~65
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.
| Name | Type | Req | Description |
|---|---|---|---|
| market | object | yes | Market document. |
No output schema declared.
No examples provided.
verify_receipt Verify an agent payment receipt ~126
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.
| Name | Type | Req | Description |
|---|---|---|---|
| public_key | string | yes | Ed25519 public key, base64url (from keygen) or 64 hex chars. |
| receipt | object | yes | The receipt document as JSON. |
No output schema declared.
No examples provided.
How do I install the nidhipati — proof of spend for AI agents MCP server?
nidhipati — proof of spend for AI agents runs locally as a container image, launched with docker run --rm -i ghcr.io/mrpkk/nidhipati:0.6.0. Ready-made configuration for Claude, Cursor, VS Code, Codex and 3 more is on this page, copied from each client's own documentation.
oci · ghcr.io/mrpkk/nidhipati
claude mcp add mrpkk-nidhipati -- docker run --rm -i ghcr.io/mrpkk/nidhipati:0.6.0
What is the nidhipati — proof of spend for AI agents MCP server?
nidhipati — proof of spend for AI agents is an MCP server listed in the public MCP registry as io.github.mrpkk/nidhipati. Proof-of-spend and reserve checks for AI agents: receipts, spend audit, NAV, reconciliation. This page covers its container image (ghcr.io/mrpkk/nidhipati).
Is the nidhipati — proof of spend for AI agents MCP server safe to use?
nidhipati — proof of spend for AI agents scores 42 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the nidhipati — proof of spend for AI agents MCP server expose?
nidhipati — proof of spend for AI agents exposes 5 tools: verify_receipt, audit_spend, reserve_report, resolve_report, reconcile_books. Their descriptions and schemas cost roughly 544 tokens of context every time the server is loaded.
Is the nidhipati — proof of spend for AI agents MCP server still maintained?
nidhipati — proof of spend for AI agents is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.