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faultbench

faultbench

by mit-hun-k
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

issue_refund

Create a refund for an order by specifying order ID, amount, and creation time. Use this action to process returns or cancellations.

Instructions

issue_refund: create on refunds

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "issue_refund"
  }
}

Input Schema

NameRequiredDescriptionDefault
amountNo
order_idNo
created_atNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

D1.6/5.0
Behavior2/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It indicates a create-like mutation but does not explain side effects, reversibility, idempotency, permissions, or whether a refund modifies an existing order. This is far too thin for a mutating tool.

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

Conciseness2/5

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

The description is extremely short, but this is under-specification rather than effective conciseness. It front-loads nothing useful and does not earn its place because it adds no operational detail.

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

Completeness1/5

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

With no annotations, no output schema, zero parameter documentation, and a vague one-line description, the tool definition is severely incomplete. An agent cannot determine how to correctly invoke issue_refund or interpret its effects.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning to any of the three parameters (amount, order_id, created_at). It does not clarify what amount represents, whether order_id is required logically, or how created_at is used. The schema itself provides only names and types.

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

Purpose2/5

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

The description 'issue_refund: create on refunds' essentially restates the tool name with the verb 'create' and the noun 'refunds'. It does not clearly state what issuing a refund involves, nor does it distinguish this tool from the sibling create_return, which appears to overlap heavily in purpose.

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

Usage Guidelines1/5

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

No guidance is given about when to use issue_refund versus alternatives like create_return, get_order, or list_orders. There is no mention of prerequisites, required context, or scenarios where a different tool should be chosen.

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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