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Compose text in EZ Lawn Biller

ez_lawn_biller_compose_text
Read-onlyIdempotent

Compose text 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_text"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is known. The description adds genuinely new behavioral context — 'opens nothing by itself' and that the return value is step-by-step instructions (screens, buttons, fields) rather than a performed action. It stops short of describing output format or length, so a 3 rather than higher.

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 operative instructions — what is returned and what to do with it — come first, which is the right ordering. The closing sentence about the missing public API is longer than it needs to be for the caller's decision, but it justifies the browser-automation path and is not padding.

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, no-output-schema tool, the description covers what it returns (step-by-step UI instructions), what it does not do (no side effects), and how to act on it (cotlet_browser_* tools). The rationale for the browser path is supplied via the API-availability note. Only sibling disambiguation is 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 takes zero parameters, so the baseline is 4. Nothing in the description misrepresents the parameter surface, and it correctly frames the tool as taking no user input beyond the implicit task context.

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 resource (text in EZ Lawn Biller) and, importantly, corrects the misleading verb by stating the tool 'Returns how to do it in EZ Lawn Biller step by step' and 'opens nothing by itself.' That reframes it from an action tool to a procedural-guidance tool, which an agent needs. It does not explicitly contrast with ez_lawn_biller_compose_email or ez_lawn_biller_send_text, leaving the 'text vs email vs send' distinction to inference.

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?

The workflow guidance is useful: get steps here, then execute with the cotlet_browser_* tools in the user's Cloud Browser. However, there is no explicit when-to-use/when-not-to-use or routing advice against the closely named siblings (compose_email, send_text, text_all), so the agent must guess which of these to call first.

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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