EZ Lawn Biller by Cotlet

Cancel run in EZ Lawn Biller

ez_lawn_biller_cancel_run

Read-onlyIdempotent

Cancel run 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_cancel_run"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.3/5.0

Behavior5/5

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

The description adds substantial behavior beyond the annotations: it returns step-by-step UI instructions, does not open or change anything itself, and requires subsequent cotlet_browser_* tools for the actual cancellation. It also explains that no public EZ Lawn Biller API was found, justifying the browser-based workflow.

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?

The description is front-loaded with the main purpose and execution flow, and it avoids unnecessary repetition. The final API-availability sentence is somewhat verbose and only indirectly relevant to invocation, but it supports the browser-workflow rationale.

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

Completeness5/5

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

For a zero-parameter, instruction-returning tool without an output schema, the description covers the essential context: what it returns, what it does not do, and which tools perform the actual operation. No critical information for correct invocation appears to be missing.

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 has zero parameters and full schema coverage, so there are no parameter meanings to clarify. The baseline for a no-parameter tool is 4, and the description does not need to add parameter semantics.

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 clearly identifies the specific resource and action: canceling a run in the user's EZ Lawn Biller account. It also clarifies that the tool returns instructions rather than performing the cancellation itself, though it does not explicitly distinguish this from a sibling such as ez_lawn_biller_cancel.

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 explains the intended flow: call this tool to get step-by-step instructions, then use cotlet_browser_* tools in the user's EZ Lawn Biller Cloud Browser to perform the action. It does not provide explicit when-not-to-use guidance or name sibling alternatives, but the context is clear enough for an agent.

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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