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xpayr_reject_agent_intent

Rejects a pending AI agent payment intent in the merchant approval queue, recording an audit reason so reviewers can document and decline risky autonomous payments.

Instructions

Reject a pending AI agent payment intent in the merchant approval queue with an audit reason.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "intent_id": "<intent_id>"
    },
    "name": "xpayr_reject_agent_intent"
  }
}

Input Schema

NameRequiredDescriptionDefault
reasonNoReviewer rejection reason.
intent_idYesPending agent intent ID to reject.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full disclosure burden. It does add real context beyond structured fields: the 'pending' state precondition and the fact that the reason is recorded for audit. However, it omits whether rejection is terminal or reversible, what happens to the intent or any held funds afterward, and what permissions are required for a mutation of this kind.

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?

A single sentence with zero filler, front-loading the mutating verb and the resource, then the scope qualifier. Nothing in it is redundant with the tool name alone, and every clause earns its place.

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 two-parameter mutation with fully documented schema and no output schema, the description covers the essentials: what is rejected, which queue it lives in, the pending precondition, and that the reason is auditable. The remaining gap is post-condition behavior — whether the intent becomes permanently rejected and whether the agent or merchant is notified — which matters for a state-changing tool.

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 both parameters are already documented in the input schema and the baseline is 3. The description adds only the phrase 'with an audit reason', which slightly clarifies that the reason is persisted for audit purposes rather than passed through transiently, but adds no format, length, or ID-shape constraints beyond the schema.

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

Purpose4/5

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

The description names a specific verb and resource ('Reject a pending AI agent payment intent') plus a scoping detail ('merchant approval queue'), so the operation is unambiguous. It does not explicitly contrast itself with the obvious complement, xpayr_approve_agent_intent, or with xpayr_bridge_agent_intent_to_checkout, leaving sibling routing to inference.

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

Usage Guidelines3/5

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

Usage is implied by the qualifier 'pending' — the agent can infer this applies only to intents awaiting review in the queue, not to already-approved or completed ones. There is no explicit when-not guidance, no statement of prerequisites or required reviewer role, and no named alternative, so the agent must infer the decision boundary between approve and reject.

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