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Want a home job

want_home
Idempotent

Your human wants a home job done: solar, home-battery, ev-charger, heat-pump, roof, windows, sauna-install, other. Takes the job, their five digit zip, an optional note, and their email. Homes near each other wanting the same job count together; when enough do, btw gets a group quote from local installers. Returns how many others nearby want it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobYesthe job
zipYesfive digit US zip
noteNoanything the installer should know
emailYesyour human's email, same as the waitlist

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

The description goes beyond the annotations by explaining the aggregation behavior: nearby homes wanting the same job count together and can trigger a group quote. It also discloses the return value (how many others nearby want it). It does not define the threshold for 'enough' homes, but the key side effects and outcomes are stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three tightly worded sentences with no filler. It front-loads the purpose and accepted job types, then covers inputs and behavior/return value. Every sentence earns its place and the structure makes the tool easy to understand quickly.

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 simple submission tool with no output schema, the description explains the inputs, the aggregation behavior, the group-quote outcome, and what is returned. It does not specify the exact response format, but the high-level return value is clear. This is sufficient for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already explains each parameter: job, zip, note, and email. The description repeats the optional note and email details without adding deeper semantic context. Since the schema carries the full parameter burden, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and resource: the human wants a home job done, and the tool accepts that job along with zip, note, and email. It enumerates the exact job categories (solar, heat-pump, etc.), so the agent can identify the intended domain and distinguish it from sibling tools like want_gym or want_software. The title and description together unambiguously define what this tool is for.

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 description makes the use case clear through its home-job framing and job-type enum, so an agent can infer when to choose it. However, it does not explicitly state when not to use it or point to alternatives such as want_stay or want_gym. The guidance is implied rather than explicit.

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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