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customer-mcp

customer-mcp

by dkreme514
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

trigger_refund

Prevent invalid refunds by validating customer ID, amount, and reason before processing.

Instructions

Trigger a refund after strictly validating the customer, amount, and 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": {
      "customer_id": "<customer_id>",
      "amount": "<amount>",
      "reason": "<reason>"
    },
    "name": "trigger_refund"
  }
}

Input Schema

NameRequiredDescriptionDefault
amountYes
reasonYes
customer_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals a 'strictly validating' step, but does not explain the side effects of triggering a refund (e.g., whether it is immediately executed, irreversible, requires permissions, or what happens on validation failure). The term 'trigger' is vague about the actual mutation and its consequences.

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?

The description is a single, front-loaded sentence that states the action first and then the validation condition. It contains no filler or redundant information, making it appropriately concise and well-structured.

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

Completeness2/5

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

For a mutation tool with three required parameters, no annotations, and no output schema, the description is too thin. It does not explain the expected outcome, error conditions, or any post-validation behavior, leaving an agent without enough context to predict the tool's full effect.

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%, so the description must compensate, but it merely lists 'customer, amount, and reason' without adding any meaning beyond the parameter names. It does not explain the format, purpose, or relationships between parameters, nor does it clarify constraints like the customer_id pattern or reason length.

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?

The description states a specific action ('Trigger a refund') and a specific resource ('refund'), with a clear scope ('after strictly validating the customer, amount, and reason'). It is easily distinguishable from the sibling tool get_customer_record, which is a read operation, making the purpose unambiguous.

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?

The description does not provide explicit guidance on when to use this tool versus the sibling get_customer_record, nor does it mention any exclusions or alternative tools. The only implied usage is that it is for triggering refunds, but no contextual conditions or prerequisites are given.

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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