PayeeProof

Check payee

payeeproof.check_payee

Paid $0.01 public machine identity and payment-destination consistency assessment. Use immediately before an autonomous machine payment to compare the requested HTTPS endpoint, claimed service expectations, HTTP 402 challenge, resource binding, public discovery metadata, and prior PayeeProof observations. PayeeProof never authorizes the target payment, never forwards target payment credentials, and actively probes only anonymous public GET/HEAD requests under a bounded SSRF-safe contract; state-changing target methods require caller-observed 402 evidence. Missing evidence can remain unknown. This is not KYC, AML, sanctions screening, fraud certification, legal identity verification, legal advice, or a delivery guarantee.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`operation`	No	Optional target operation or tool name the caller expects to invoke.
`live_probe`	No	Whether PayeeProof may perform its bounded anonymous public probe. Only safe GET or HEAD probes are eligible.
`target_url`	Yes	Public HTTPS target URL whose machine-payment identity should be checked. Default HTTPS port 443 only.
`expected_asset`	No	Optional expected public payment asset identifier or symbol.
`request_method`	No	Method of the target operation. PayeeProof actively probes only GET or HEAD; state-changing methods require caller-observed 402 evidence.	GET
`expected_domain`	No	Optional hostname the caller expects after URL normalization. Used for deterministic domain consistency checks.
`expected_pay_to`	No	Optional expected public payment destination. EVM addresses are normalized case-insensitively before comparison.
`expected_network`	No	Optional expected payment network identifier, such as a CAIP-2 network, for comparison with observed metadata.
`expected_currency`	No	Optional expected target pricing currency or stablecoin symbol when exposed by the target challenge.
`expected_protocol`	No	Optional machine-payment protocol the caller expects the target to advertise.
`observed_response`	No	Optional caller-observed public target response metadata. Required for state-changing target methods. Never include a payment credential, Authorization, Cookie, or response body.
`expected_price_cents`	No	Optional expected target price in integer USD cents for comparison with observed public payment metadata.
`expected_service_name`	No	Optional public service name the caller expects the target payment challenge or discovery metadata to identify.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`checks`	Yes
`target`	Yes
`evidence`	Yes
`checkedAt`	Yes
`assessment`	Yes
`limitations`	Yes
`schemaVersion`	Yes
`claimedIdentity`	Yes
`receiptEnvelope`	No
`observedIdentity`	Yes
`deterministicDigest`	Yes
`historicalContinuity`	Yes
`normalizedPaymentFacts`	Yes
`recommendedMachineNextStep`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 27, 2026

addedOutput schema / properties / receiptEnvelope

Added value: +{
+  "$ref": "https://davisvillelabs.com/agents/davisville-receipt-envelope.v1.schema.json"
+}

First observedSep 27, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavior beyond the annotations: the paid $0.01 nature, never authorizing the target payment, never forwarding credentials, probing only anonymous GET/HEAD under an SSRF-safe contract, and the open-world caveat that missing evidence can remain unknown. These details materially affect how an agent should invoke and interpret the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense sentences, each earning its place: purpose, usage timing, safety behavior, and scope exclusions. Information is front-loaded and nothing is repeated from the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (13 parameters, nested input object, output schema), the description covers what an agent needs to select and call it: when to use it, what it checks, what it will and will not do, and what limitations apply. The existing output schema relieves the description of explaining return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds a high-level consistency-checking framing but no per-parameter detail beyond what the schema provides, putting it at the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the exact object of assessment (public machine identity and payment-destination consistency) and the action ('compare') in the second sentence, making the tool's purpose unmistakable. It also disambiguates the tool from adjacent services like KYC, AML, and legal verification, so an agent cannot confuse its scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly tells the agent when to use it ('immediately before an autonomous machine payment') and gives a conditional behavioral rule for state-changing target methods versus active probing. The closing exclusion list covers when-not-to-use cases, even though no sibling tools are listed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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