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order_production_harden

Opslane Production Harden — 149 USD, paid per call. Pay in USDC on Base via x402, or RLUSD/XRP on XRPL via POST https://spec72.dev/api/a2a/xrpl/quote/production-harden (body {currency, inputs}). Async: returns order_id + status_url.

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": {
      "inputs": "<inputs>"
    },
    "name": "order_production_harden"
  }
}

Input Schema

NameRequiredDescriptionDefault
buyerNo
inputsYesRequired: repo_url, what_breaks
callback_urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a solid job: it discloses the per-call cost (149 USD), the payment rails, the async nature of execution, and the return shape (order_id + status_url). It falls short on authorization requirements, what happens after payment, whether orders can be cancelled/refunded, and the role of callback_url.

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

Conciseness4/5

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

The definition is compact and front-loads the service name and price before the payment mechanics. The URL/body fragment is dense but every clause carries information; nothing is filler.

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

Completeness3/5

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

For a paid async order tool with nested inputs and no output schema, the description conveys cost, payment options, and the immediate return values, which is the essential core. It still leaves gaps on the buyer object, callback_url, authorization, and what the delivered 'harden' work entails, so it is adequate rather than complete.

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 coverage is only 33%, so the description should compensate more than it does. It clarifies that the quote body takes {currency, inputs} and the schema adds repo_url/what_breaks under inputs, but the buyer object and callback_url are left completely unexplained in both places.

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

Purpose3/5

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

The description identifies the offering ('Opslane Production Harden'), the price, and the mechanics of paying and ordering, so an agent can tell this is a purchase/order tool for a specific service. However, it never says what the service actually does beyond the label, and it does not distinguish this order tool from siblings like order_audit or order_stack_check — the only differentiator is the service name in the title.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not-to-use guidance, and no routing hints toward the sibling order_* tools for other services. The only implicit usage information is the choice of payment rail (USDC on Base via x402 vs RLUSD/XRP on XRPL), which tells an agent how to pay but not when this tool is the right one to pick.

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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