EZ Lawn Biller by Cotlet

Export items in EZ Lawn Biller

ez_lawn_biller_export_items

Read-onlyIdempotent

Export items 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_export_items"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4/5.0

Behavior4/5

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

Adds real context beyond the annotations: the tool performs no action itself, returns screens/buttons/fields guidance, and the API note explains why a browser path is required (no public API, 404s on /api, /developers, /docs, QuickBooks as sole integration). This is exactly the kind of constraint annotations cannot express. It does not describe response size/format beyond "step by step".

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?

Front-loads the purpose and the no-side-effects caveat, then the API rationale. The API research sentence is somewhat verbose but earns its place by justifying the browser-tool handoff and preventing an agent from hunting for a nonexistent API.

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?

No output schema exists, and the description does explain what comes back (step-by-step screens, buttons, fields) and what to do next. For a zero-parameter, read-only guidance tool this is close to complete; only the exact response shape remains unspecified.

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?

Zero parameters, so there is nothing for the description to disambiguate. Baseline applies; the description correctly implies the tool needs no input because it simply returns a canned procedure.

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?

States a specific verb and resource ("Export items") scoped to "the user's own EZ Lawn Biller account", and clarifies the tool returns step-by-step instructions rather than performing the export. The Items resource differentiates it from the export_customers/export_payments siblings by name, though no sibling is called out explicitly.

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

Usage Guidelines4/5

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

Explicitly routes the agent: this tool returns the procedure, then the actual work is done "with the cotlet_browser_* tools in the user's EZ Lawn Biller Cloud Browser". It also states the negative case ("opens nothing by itself"), so an agent knows not to expect side effects. No explicit when-not-to-use beyond that.

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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