dns-of-money
Server Details
Resolve, discover & pay pay: aliases for AI agents; returns a signed OFAC-screen attestation.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 3 of 3 tools scored.
Each tool targets a distinct aspect of the pay: alias ecosystem: discover_agent returns agent capabilities, get_payment_requirements returns payment specifics, and resolve_alias returns routing metadata. No functional overlap.
All tools follow a consistent verb_noun pattern (discover_agent, get_payment_requirements, resolve_alias) with clear, descriptive verbs. No mixing of conventions.
Three tools is a minimal set but aligned with the narrow domain of DNS of Money payment discovery and requirements. Not excessive, though a larger surface could cover additional steps like mandate management.
The tools cover agent discovery, payment requirement retrieval, and alias resolution, but lack a tool for actually submitting payments or managing mandates. This leaves a notable gap in the full payment workflow.
Available Tools
3 toolsdiscover_agentAInspect
Get the AP2/A2A agent-commerce block for a pay: address — the DNS of Money equivalent of an A2A agent-card lookup. Use this during agent discovery before initiating a mandate flow. Returns agent capabilities only; NO payment credentials. Example: discover_agent('pay:acme.user').
| Name | Required | Description | Default |
|---|---|---|---|
| alias_uri | Yes | The pay: address to discover. E.g. 'pay:acme.user'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns only agent capabilities and NO payment credentials, making the read-only nature clear. It does not mention auth or rate limits, but for a simple lookup this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences and an example example. Every word adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single required parameter and no output schema, the description fully explains the tool's purpose, usage context, input format, and output scope. No information is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter coverage is 100% via schema description. The description adds an example call ('discover_agent('pay:acme.user')') and explains the alias_uri format, providing value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the AP2/A2A agent-commerce block for a pay: address'. It distinguishes from siblings like 'get_payment_requirements' and 'resolve_alias' by specifying it is for agent discovery before a mandate flow, and it returns capabilities not credentials.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this during agent discovery before initiating a mandate flow' and clarifies what it returns and does not return. It lacks explicit alternatives or when-not-to-use, but the context with sibling tool names implies usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_payment_requirementsAInspect
Get x402 PaymentRequirements for paying a pay: alias. Returns the 402 accepts block (scheme, network, payTo, asset, amount, invoiceId) an x402 agent needs to construct and submit the payment. currency='XRP' settles on the XRP Ledger (amount in drops); currency='USDC' settles USDC on Algorand via the GoPlausible facilitator (amount in atomic micro-USDC, feePayer in extra). The deterministic resolver chooses the rail; this tool never moves money. Example: get_payment_requirements('pay:acme.user', amount='1.5').
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Amount to pay — decimal string in the asset's display unit (XRP). | |
| currency | No | Asset to pay in: 'XRP' (XRPL) or 'USDC' (Algorand). Defaults to XRP. | |
| alias_uri | Yes | The pay: address to pay. E.g. 'pay:acme.user'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explicitly states 'this tool never moves money' (indicating read-only, no side effects) and explains the deterministic resolver chooses the rail. This provides essential behavioral context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact yet rich, with three focused sentences and a concrete example. Every sentence adds critical information without redundancy. The structure front-loads the purpose and then provides details, making it easy for an AI agent to quickly grasp functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description fully explains the return structure (402 accepts block fields: scheme, network, payTo, asset, amount, invoiceId) and covers both currency cases with settlement details. It fully prepares an agent to use the tool correctly without gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, so baseline is 3. The description adds significant value by explaining that 'amount' is a decimal string in display unit, 'currency' defaults to XRP and affects settlement rails, and provides practical details like drops and atomic micro-USDC, which go beyond the schema's descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Get x402 PaymentRequirements'), the target resource ('pay: alias'), and what the return value contains (402 accepts block). It distinguishes this tool's purpose from generic resolution by explaining it returns structured payment requirements for x402 agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for when to use this tool (to obtain payment requirements for x402 payments) and includes an example. However, it does not explicitly state when not to use it or mention alternatives like discover_agent or resolve_alias, which are siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_aliasAInspect
Resolve a human-readable financial alias (pay: address) to payment endpoints and routing metadata. Call this before initiating any payment to a pay: address. Returns the preferred rail, fallback endpoints, an ISO 20022 hint, and a compliance signal. Field visibility depends on the caller's API-key tier (anonymous callers see status only). Example: resolve_alias('pay:acme.user').
| Name | Required | Description | Default |
|---|---|---|---|
| amount | No | Optional payment amount (decimal string) — refines rail scoring. | |
| currency | No | Optional ISO 4217 currency code. Defaults to USD. | |
| alias_uri | Yes | The pay: address to resolve. E.g. 'pay:acme.user'. | |
| preferred_rail | No | Optional rail hint (e.g. 'xrpl', 'fednow'). Advisory only. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return fields (preferred rail, fallback endpoints, ISO 20022 hint, compliance signal) and tier-based visibility. Being a read-only resolution, it is safe. No annotations provided, so description covers well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no filler. Front-loaded purpose, then usage and output, ends with example. Every sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description explains output fields. Covers usage, parameters, and edge cases (tier visibility). No gaps for a tool with 4 params and simple behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds meaning: amount refines rail scoring, preferred_rail is advisory, alias_uri format with example. Adds clarity beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it resolves a pay: address to payment metadata, with specific verb and resource. Distinguishes itself from siblings by explicitly stating its role in payment initiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to call before initiating a payment to a pay: address. Mentions API-key tier visibility. Does not explicitly differentiate from siblings, but context signals provide sibling names.
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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