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Stabledrop escrow payments

Payment Qr

payment_qr

A scannable code for a payment request, so nobody retypes an address.

Pass the payment_uri from prepare_escrow_payment. Returns a QR a phone wallet can scan, with the token, network, destination and amount already in it.

⚠️ A SEPARATE TOOL RATHER THAN PART OF prepare, because the two have different audiences. prepare answers a machine and has to stay parseable; an image in its result would make the structured fields something a client has to dig for. A person who needs the code asks for it.

⚠️ AND IT ENCODES THE URI, NOT THE BARE ADDRESS. A QR holding only an address leaves the amount and the token to be entered by hand, which is the part worth removing — an escrow funded with the wrong figure is not the escrow these terms derive to, and the money sits at an address nothing can deploy to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payment_uriYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does disclose the output (a QR containing token, network, destination, and amount) and the critical encoding behavior (URI, not bare address). It does not explicitly state side-effect status or failure behavior, but for a pure encoding/rendering tool this is reasonably transparent.

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

Conciseness3/5

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

The first two sentences are concise and front-loaded, but the two all-caps warnings are verbose and partly explain design rationale rather than operational requirements. The content is useful, but the description could be tightened without losing meaning.

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

Completeness4/5

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

For a one-parameter tool with no output schema and no annotations, the description is largely complete: it names the input source, describes the output, and warns about the key misuse. It could be more explicit about the exact QR/image format returned and error cases, but these are minor gaps.

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

Parameters5/5

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

The single `payment_uri` parameter is given provenance ('from `prepare_escrow_payment`') and semantic content (token, network, destination, amount). Since the input schema only says 'string' and schema description coverage is 0%, this fully compensates for the missing schema documentation.

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?

States a specific verb and resource: it 'Returns a QR a phone wallet can scan' for a payment request, with a clear purpose. It also distinguishes itself from prepare_escrow_payment by audience ('A person who needs the code asks for it').

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?

Explicitly instructs the caller to pass the `payment_uri` from `prepare_escrow_payment` and explains when the tool is appropriate. It also clarifies that it is intentionally separate from `prepare`, so an agent knows not to expect this output embedded in the machine-readable result.

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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