EZ Lawn Biller by Cotlet

Usage Trends in EZ Lawn Biller

ez_lawn_biller_usage_trends

Read-onlyIdempotent

Usage Trends 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_usage_trends"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description reinforces this by stating it 'opens nothing by itself', which prevents an agent from assuming the trend is retrieved directly. It also explains why a browser fallback exists (no public API found, endpoints 404), useful context beyond the annotations.

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?

Purpose is front-loaded, but the middle clause ('Returns how to do it ... and opens nothing by itself; then do it with...') crams the return contract and the follow-up workflow into one awkward sentence. The dated API-audit sentence supports the browser-fallback rationale but reads as boilerplate appended to the definition.

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?

With no parameters and no output schema, the description carries the burden of saying what comes back, and it does ('step by step (screens, buttons, fields)'). What it lacks is any hint about the shape or granularity of that guide, or what to do if the named screen does not exist in the user's account.

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 no parameters, so the baseline of 4 applies. The description correctly implies no inputs are needed, matching the empty schema.

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 states a specific resource ("Usage Trends in the user's own EZ Lawn Biller account") and clarifies the tool returns a step-by-step guide rather than performing an action. It does not differentiate from adjacent siblings such as ez_lawn_biller_chemical_usage or ez_lawn_biller_sms_usage, so an agent must infer which 'usage' topic it needs.

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?

It gives explicit sequencing: this tool opens nothing by itself, and the actual work is then done with the cotlet_browser_* tools in the Cloud Browser. What is missing is any exclusion or comparison against the other usage-related siblings, so the when-not guidance is absent.

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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