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Compose email in EZ Lawn Biller

ez_lawn_biller_compose_email
Read-onlyIdempotent

Compose email 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_compose_email"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, idempotentHint=true and non-destructive, and the description is consistent with them while adding the vital behavioral fact that it "opens nothing by itself." It also discloses the shape of the return value (step-by-step screens, buttons, fields) despite there being no output schema, though it says nothing about output length or failure modes when the Cloud Browser is unavailable.

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 operational content is front-loaded and tight, but the third sentence spends a lot of words on a dated audit trail (ezlawnbiller.com/api, /developers, /docs returning 404, FAQ naming QuickBooks). That justifies why browser tools are needed but is far more detail than an agent needs to invoke the tool.

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-param, read-only guidance tool with rich annotations and no output schema, the description covers what it does, what it returns, and what to do next. What is missing is prerequisite state—e.g., what to do if the user has not yet connected the Cloud Browser—but nothing essential to a correct call is absent.

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, which is the baseline-4 case; there is nothing for the description to disambiguate. It does implicitly confirm the parameterless nature by describing the call as purely instructional.

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 a specific verb and resource ("Compose email in the user's own EZ Lawn Biller account") and immediately clarifies the crucial twist: it returns step-by-step instructions rather than performing the action. It does not name a sibling such as ez_lawn_biller_compose_text or ez_lawn_biller_send_email to contrast against, so an agent must still infer the boundary from the stated behavior.

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 clear when-to-use context and an explicit follow-on workflow: call this to get the screens/buttons/fields, then act via the cotlet_browser_* tools in the EZ Lawn Biller Cloud Browser. It stops short of exclusions, e.g., when to prefer this over the sibling send_email or email_selected tools.

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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