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Email All Statements in EZ Lawn Biller

ez_lawn_biller_email_all_statements
Read-onlyIdempotent

Email All Statements 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_email_all_statements"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, and the description is fully consistent with them by stating the tool 'opens nothing by itself.' It adds real context beyond the annotations: the return value is procedural instructions (screens, buttons, fields) rather than an action, plus the absence of an official 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 behavior and the handoff to cotlet_browser_* tools are front-loaded well. The trailing API-audit detail (404s on /api, /developers, /docs, FAQ naming QuickBooks) is over-long for what an agent needs to decide, and the sentence structure is dense with semicolons.

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, non-destructive guidance tool with no output schema, the description covers everything an agent needs: what it returns (step-by-step screen/button/field instructions), what it does not do (open or send anything), and which tool family to use next.

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; the baseline of 4 applies. No parameter claims are made that could mislead.

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 the specific task (email all statements in the user's EZ Lawn Biller account) and, crucially, discloses that the tool does not actually perform it: it 'Returns how to do it in EZ Lawn Biller step by step ... and opens nothing by itself.' That resolves the tension with the imperative-sounding name, though an agent skimming the name alone could still misread it as an action tool.

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?

It gives a clear follow-on path — perform the steps with the cotlet_browser_* tools in the user's Cloud Browser — which is genuinely useful routing. However, it never distinguishes this from close siblings such as ez_lawn_biller_email_selected or ez_lawn_biller_send_email, and offers no explicit when-not guidance.

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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