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ShearQuery — Barber & Beauty Industry Data

Draft a Google post, now or scheduled

propose_post

Draft a Google post (up to 1500 characters) with a button, optionally a photo, optionally an offer with dates and a code, and optionally a time to publish in the future. Base it on something true about the business — a service, a real review, holiday hours — never an invented promotion. Creates a DRAFT only — nothing on Google changes. Show the owner the draft this returns, word for word, and publish it with publish_change only after they say yes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
offerNo
buttonNoDefault LEARN_MORE with a url, otherwise CALL.
photo_urlNoPublic https image link. The listing's own photo links from my_photos work.
button_urlNohttps link for every button except CALL.
publish_atNoISO 8601 date-time to publish later (10 minutes to 90 days out). Omit to publish on approval.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and destructiveHint=false; the description adds valuable context beyond that by clarifying this is a DRAFT-only call ('nothing on Google changes'), which explains why a write is non-destructive. It also discloses the 1500-character cap and stays silent on return payload shape or error modes.

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?

Three sentences, front-loaded with purpose and then the approval workflow. Dense but each clause carries operational information (length cap, scheduling, content authenticity rule, handoff). Slightly long, but nothing is filler.

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 6-param, nested-object mutation tool with no output schema and no annotations on safety specificity beyond readOnly/destructive, the description covers the draft-only semantics, the approval loop, and the content constraint. It stops short of describing what the returned draft contains, though 'the draft this returns' is at least acknowledged.

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?

Schema coverage is 67%, so the schema documents most params; the description still adds the 1500-character limit on text and the scheduling intent for publish_at. It does not add format detail for offer fields or button_url beyond what the schema already states.

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?

Specific verb+resource (draft a Google post) with the concrete content envelope spelled out (button, optional photo, optional offer, optional schedule). It also distinguishes itself from the sibling it hands off to (publish_change), so an agent can route without opening schemas.

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?

Gives clear workflow guidance: show the returned draft to the owner word for word, then publish with publish_change only after approval. Also constrains content sourcing ('never an invented promotion'). No explicit when-not-to-use or comparison against other propose_* siblings, so it stops short of a 5.

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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