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Issue Credit in EZ Lawn Biller

ez_lawn_biller_issue_credit
Read-onlyIdempotent

Issue Credit in the user's own EZ Lawn Biller account from ChatGPT, Claude or any AI agent. Returns how to do it in EZ Lawn Biller step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's EZ Lawn Biller Cloud Browser. EZ Lawn Biller's official API: No publicly documented EZ Lawn Biller API was found on 11 Nov 2026: ezlawnbiller.com/api, /developers and /docs returned 404, and its FAQ and manual name QuickBooks as the only integration.

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": "ez_lawn_biller_issue_credit"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and idempotentHint=true, and the description is consistent with them by stating it 'opens nothing by itself' and only returns instructions. It adds real context beyond the annotations: who executes the action (the cotlet_browser_* tools in the Cloud Browser) and the absence of a documented public API, which explains why browser automation is required.

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 core action and the step-by-step/no-side-effect clarification are front-loaded and earn their place. However, the trailing API-research paragraph (404s on specific paths, a dated investigation note) is verbose and largely incidental to invoking the tool correctly.

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 zero-parameter guidance tool with annotations covering the safety profile and no output schema, the description supplies what an agent needs: what comes back, that nothing is executed, and which tools complete the task. The only gap is that it never states how the credit amount or customer is conveyed to the subsequent browser step.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline is high. The description correctly implies no inputs are needed, though it does not say what identifying context (e.g., which customer/account) the follow-up browser step requires.

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 action and resource (issue a credit in EZ Lawn Biller) and clarifies the crucial nuance that the tool only returns step-by-step instructions rather than performing the action itself, which resolves the mismatch between the name and its read-only behavior. It does not differentiate against the obvious sibling ez_lawn_biller_issue_charge, so an agent must infer the credit-vs-charge distinction from the names alone.

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 through a two-step workflow: call this to get the screens/buttons/fields, then execute with cotlet_browser_* tools in the user's Cloud Browser. There is no explicit statement of when to prefer this over the issue_charge sibling or any exclusion conditions, leaving the alternative selection to inference.

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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