EZ Lawn Biller by Cotlet

Export Dashboard in EZ Lawn Biller

ez_lawn_biller_export_dashboard

Read-onlyIdempotent

Export Dashboard 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_dashboard"
  }
}

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 mark it read-only, idempotent, non-destructive, and not open-world; the description adds meaningful behavior by stating that it opens nothing itself and only returns procedural steps. It also notes that no official API exists, explaining the browser-based follow-up, though it does not detail response format or limits.

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 purpose and next step are front-loaded, but the third sentence spends substantial space on API audit details such as the date, 404 URLs, and QuickBooks mention. These details are tangential to invoking this specific tool, making the definition more verbose than necessary.

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, read-only instruction-returning tool with no output schema, the description supplies enough context: what it returns and what to do next. The API note further explains why browser automation is required, though the return format could be slightly more explicit.

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?

There are zero input parameters, and schema coverage is complete, so there is nothing parameter-specific to explain. The baseline for a no-parameter tool is 4.

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 the specific action and resource ('Export Dashboard in the user's own EZ Lawn Biller account') and clarifies that it returns step-by-step instructions rather than performing the export. It does not directly name which sibling export tool to use instead, so sibling differentiation is weaker than a 5.

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?

Makes the workflow clear: call this to get EZ Lawn Biller screens/buttons/fields, then execute in the user's Cloud Browser with cotlet_browser_* tools. There are no explicit exclusions or comparisons to the many other export_* siblings, so it lacks a 5-level when-not/alternative statement.

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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